Crude oil scheduling result generation method and device, and nonvolatile storage medium

Abstract

The application discloses a crude oil scheduling result generation method and device, a nonvolatile storage medium and an electronic device. The method comprises the following steps: solving a target model according to basic data and crude oil proportioning information to obtain a crude oil scheduling result; in the case that the target model fails to be solved, processing a plurality of time intervals of a crude oil scheduling period to generate a plurality of target crude oil scheduling periods; and inputting the target crude oil scheduling period into the target model to generate a target crude oil scheduling result. The application solves the technical problem that the prior art can only obtain a local optimal solution of a crude oil scheduling overall process by cutting each process in the crude oil scheduling overall process into a separate event, separately processing each event, and splicing the processed events to generate the crude oil scheduling overall process.

Description

Technical Field

[0001] This application relates to the field of industrial information technology, specifically to a method and apparatus for generating crude oil dispatching results, a non-volatile storage medium, and an electronic device. Background Technology

[0002] Raw material costs account for approximately 90% of the total costs for oil refining enterprises; therefore, crude oil dispatching and transportation are crucial aspects of these enterprises. Currently, the crude oil dispatching plans of petrochemical refining enterprises are generally structured by first establishing a long-term plan (such as annual or monthly plans), then allocating these plans evenly to rough short-term plans (such as weekly, daily, or shift plans) based on the long-term plan's objectives, and finally, having dispatchers make detailed adjustments to these rough short-term plans. On the one hand, dispatchers need to consider the dispatching arrangements of upstream and downstream entities to meet their production dispatching needs; on the other hand, they need to track the plans and adjust them promptly based on uncertainties such as current production dispatching progress, market price fluctuations, unit shutdowns and maintenance, ship delays, and changes in environmental policies.

[0003] In summary, an unreasonable crude oil scheduling plan not only fails to guarantee stable long-term refinery production but may also lead to safety issues in severe cases. Current technologies generate the overall crude oil scheduling process by dividing each step of the overall process into individual events, processing each event separately, and then piecing together the processed events. However, this method suffers from the technical limitation of only obtaining a locally optimal solution to the overall crude oil scheduling process.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This application provides a method and apparatus for generating crude oil dispatching results, a non-volatile storage medium, and an electronic device, to at least solve the technical problem in the prior art that the overall crude oil dispatching process is divided into individual events, each event is processed separately, and the processed events are spliced ​​together to generate the overall crude oil dispatching process, which results in only obtaining a local optimum of the overall crude oil dispatching process.

[0006] According to one aspect of the embodiments of this application, a method for generating crude oil dispatch results is provided, comprising: solving a target model based on basic data and crude oil blending information to obtain crude oil dispatch results, wherein the target model is used to indicate the relationship between the basic data, crude oil blending information, and crude oil dispatch results; the basic data includes at least one of the following: crude oil dispatch cycle, oil tanker unloading, crude oil transfer, and crude oil processing flow; the crude oil blending information is the crude oil blending ratio of multiple time intervals of the crude oil dispatch cycle; and the crude oil dispatch results include at least the oil receiving and payment dispatching arrangements of oil tankers and terminal tanks in each time interval; if the target model fails to be solved, processing the multiple time intervals of the crude oil dispatch cycle to generate multiple target crude oil dispatch cycles; and inputting the target crude oil dispatch cycles into the target model to generate target crude oil dispatch results.

[0007] Optionally, multiple time intervals of the crude oil scheduling cycle are processed, including: processing the start time interval to the end time interval of multiple time intervals of the crude oil scheduling cycle in sequence.

[0008] Optionally, the starting time interval to the ending time interval of multiple time intervals of the crude oil scheduling cycle are processed sequentially, including: in the process of processing multiple time intervals sequentially, the first time interval among the multiple time intervals is determined as a discrete variable, and the second time interval among the multiple time intervals is determined as a continuous variable, wherein the first time interval is any interval among the starting time interval to the ending time interval, and the second time interval is the time interval among the multiple time intervals that is located after the first time interval and adjacent to the first time interval.

[0009] Optionally, before inputting the basic data and crude oil blending scheme into the target model, the method further includes: selecting target constraints from multiple constraints; and determining the target model based on the objective function and the target constraints.

[0010] Optionally, before determining the target model based on the objective function and the target constraints, the method further includes: converting the nonlinear constraints among the multiple constraints into linear constraints.

[0011] Optionally, the objective function is determined by the following parameters: the set of off-site terminal tanks, the set of on-site crude oil tanks, the set of atmospheric and vacuum distillation units, the set of time periods within the scheduling cycle, the inventory cost of terminal tanks, the feed switching cost of terminal tanks, the feed switching cost of atmospheric and vacuum distillation units, the number of switching times of terminal tanks within a time period, the number of switching times of atmospheric and vacuum distillation units within a time period, the average inventory level of terminal tanks, and the average inventory level of crude oil tanks; the objective constraints include at least one of the following: each oil tanker can only unload oil once within the crude oil scheduling cycle, oil tanker material balance, the crude oil unloaded by the oil tanker is consistent with the crude oil that can be received by the terminal tanks, the upper and lower limits of terminal tank inventory, the average inventory level of terminal tanks, the continuous oil receiving operation of a single terminal tank, and the amount of oil that can be delivered by the crude oil tanks.

[0012] Optionally, the basic data also includes: the tanker arrival schedule, information on terminal tanks and crude oil tanks, the number of pipelines, the upper and lower limits of the atmospheric and vacuum distillation unit's processing capacity, the crude oil blending scheme, and the unit switchover cost. Among these, the tank information includes at least: the upper and lower limits of tank storage, the initial inventory, and the inventory cost. The scheduling results also include: the scheduling arrangements from tankers to terminal tanks, from terminal tanks to crude oil tanks, and from crude oil tanks to atmospheric and vacuum distillation units.

[0013] According to another aspect of the embodiments of this application, a non-volatile storage medium is also provided, the storage medium including a stored program, wherein the program, when running, controls the device where the storage medium is located to execute the above-mentioned crude oil scheduling result generation method.

[0014] According to another aspect of the embodiments of this application, an electronic device is also provided, including: a memory and a processor, wherein the processor is configured to run a program stored in the memory, wherein the program executes the above-described crude oil scheduling result generation method during runtime.

[0015] In this embodiment, the target model is solved based on basic data and crude oil ratio information to obtain crude oil scheduling results. The target model indicates the relationship between the basic data, crude oil ratio information, and crude oil scheduling results. The basic data includes at least the crude oil scheduling cycle, the crude oil ratio information is the crude oil ratio for multiple time intervals within the crude oil scheduling cycle, and the crude oil scheduling results include at least the crude oil ratio information for oil tankers and terminal tanks in each time interval. If the target model fails to solve, multiple time intervals of the crude oil scheduling cycle are processed to generate multiple target crude oil scheduling cycles. By inputting the target crude oil scheduling cycles into the target model to generate target crude oil scheduling results, and by processing multiple time intervals of the crude oil scheduling cycle to generate multiple target crude oil scheduling cycles, the goal of avoiding local optima in the overall crude oil scheduling process is achieved. This realizes the technical effect of formulating a reasonable and efficient overall crude oil scheduling process, and solves the problem of existing technologies that divide the overall crude oil scheduling process into individual events, process each event separately, and then splice the processed events to generate the overall crude oil scheduling process, resulting in only local optima in the overall crude oil scheduling process. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0017] Figure 1 This is a flowchart of a method for generating crude oil dispatching results according to an embodiment of this application;

[0018] Figure 2 This is a flowchart of another method for generating crude oil dispatching results according to an embodiment of this application;

[0019] Figure 3 This is a schematic diagram of a crude oil scheduling optimization calculation process according to an embodiment of this application;

[0020] Figure 4 This is a flowchart illustrating a time interval for processing a crude oil scheduling cycle according to an embodiment of this application;

[0021] Figure 5 This is a schematic diagram of a crude oil dispatching process according to an embodiment of this application;

[0022] Figure 6 This is a structural diagram of a crude oil dispatch result generation device according to an embodiment of this application;

[0023] Figure 7This is a hardware structure block diagram of a computer terminal (or electronic device) for a crude oil dispatch result generation method provided in an embodiment of this application. Detailed Implementation

[0024] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0025] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0026] In related technology 1, with crude oil blending, inventory matching degree, and oil product switching frequency as objectives, and considering daily oil product demand and atmospheric and vacuum distillation unit yield, the oil delivery situation from terminal tanks to crude oil tanks and the crude oil blending scheduling results are calculated. Related technology 1 can meet the production needs of general refineries and can also cope well with changes in actual production. However, the possible shortcomings are: it only considers the oil delivery scheduling from terminal tanks to crude oil tanks, and the subsequent scheduling is based on the terminal tank results after calculation.

[0027] In related technology 2, the entire refinery system is divided into three parts: crude oil supply, refining production, and finished product blending and delivery. Based on discrete time, the model is constructed from the perspective of the operation mode, mode switching and transition process of the production unit. The discrete time optimization operation control of mode switching and transition process in the multi-variety finished product production scheduling of the refinery is carried out, and a scheduling model is constructed that can minimize the production cost, material storage cost and order violation penalty of the production process.

[0028] In related technology 3, a heuristic optimization algorithm is used to solve the problem. In order to solve the problem of the algorithm getting stuck in local optimization, a switching value p is set so that the algorithm can take into account both global search and local search during the iterative solution process, which effectively improves the solution quality of the algorithm.

[0029] The aforementioned related technologies all involve dividing the overall crude oil dispatching process into individual events, processing each event separately, and then concatenating the processed events to generate the overall crude oil dispatching process. However, this approach only yields a locally optimal solution to the overall crude oil dispatching process. To address this issue, this application provides a related solution, which is detailed below.

[0030] According to an embodiment of this application, a method embodiment for generating crude oil scheduling results is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0031] Figure 1 This is a flowchart of a method for generating crude oil dispatching results according to an embodiment of this application, such as... Figure 1 As shown, the method includes the following steps:

[0032] Step S102: Solve the target model based on the basic data and crude oil ratio information to obtain the crude oil scheduling result. The target model is used to indicate the relationship between the basic data, crude oil ratio information and crude oil scheduling result. The basic data includes at least one of the following: crude oil scheduling cycle, oil tanker unloading, crude oil transfer and crude oil processing flow. The crude oil ratio information is the crude oil ratio in multiple time intervals of the crude oil scheduling cycle. The crude oil scheduling result includes at least the oil tanker and terminal tanker oil receipt and payment scheduling arrangements in each time interval.

[0033] According to an optional embodiment of this application, the target model is a function (correspondence) between basic data, crude oil ratio information and crude oil scheduling results. By inputting the basic data and crude oil ratio information into the above function, the crude oil scheduling results can be obtained.

[0034] Step S104: If the target model fails to solve, process multiple time intervals of the crude oil scheduling cycle to generate multiple target crude oil scheduling cycles.

[0035] According to another optional embodiment of this application, the target model is solved. If the solution is successful, the crude oil scheduling result is output; if the solution fails, the solution is re-solved. A solution failure occurs when the target model cannot output a crude oil scheduling result or cannot output any value. During the re-solution process, multiple time intervals of the crude oil scheduling cycle are processed. Because crude oil scheduling is performed sequentially within a custom scheduling cycle, and time intervals are discrete variables, the entire scheduling cycle is divided into many intervals. Following the order of priority, the first interval is first determined as a discrete variable, and the time intervals following the first time interval are continuous variables. After optimization, the first interval is determined as the optimization result. Then, the second interval is determined as a discrete variable, and the time intervals following the second interval are continuous variables. After optimization, the second interval is determined as the optimization result, and so on, until the last time interval is processed.

[0036] For example, the crude oil dispatch cycle includes eight time intervals. When processing the first time interval, it is defined as a discrete variable, while the second to eighth time intervals are defined as continuous variables, and the first time interval is defined as the optimization result. When processing the second time interval, it is defined as a discrete variable, while the third to eighth time intervals are defined as continuous variables, and the first and second time intervals are defined as the optimization results. When processing the third time interval, it is defined as a discrete variable, while the fourth to eighth time intervals are defined as continuous variables, and the first, second, and third time intervals are defined as the optimization results, and so on, until the eighth time interval is processed, and all time intervals are defined as the optimization results.

[0037] In this step, a method combining discretization and continuous time is used, which has the technical effect of improving the efficiency of solving the target model.

[0038] Step S106: Input the target crude oil scheduling cycle into the target model to generate the target crude oil scheduling result.

[0039] In some optional embodiments of this application, for example, the crude oil scheduling cycle includes four time intervals. When processing the first time interval, the first time interval is determined as a discrete variable, the second to fourth time intervals are determined as continuous variables, and the first time interval is determined as the optimization result. Inputting the optimized first time interval into the target model yields the crude oil scheduling result corresponding to the first time. When processing the second time interval, the second time interval is determined as a discrete variable, the third to fourth time intervals are determined as continuous variables, and the second time interval is determined as the optimization result, yielding the crude oil scheduling result corresponding to the second time. When processing the third time interval, the third time interval is determined as a discrete variable, the fourth time interval is determined as a continuous variable, and the third time interval is determined as the optimization result, yielding the crude oil scheduling result corresponding to the third time. When processing the fourth time interval, the fourth time interval is determined as a discrete variable, and the fourth time interval is determined as the optimization result, yielding the crude oil scheduling result corresponding to the fourth time.

[0040] Based on the above steps, by processing multiple time intervals of the crude oil scheduling cycle, multiple target crude oil scheduling cycles are generated, thereby avoiding the generation of local optimal solutions in the overall crude oil scheduling process. This achieves the technical effect of formulating a reasonable and efficient overall crude oil scheduling process, and solves the technical problem that existing technologies divide each process in the overall crude oil scheduling process into individual events, process each event separately, and then splice the processed events together to generate the overall crude oil scheduling process, resulting in only obtaining local optimal solutions for the overall crude oil scheduling process.

[0041] According to an optional embodiment of this application, processing multiple time intervals of the crude oil scheduling cycle includes the following steps: sequentially processing the start time interval to the end time interval of the multiple time intervals of the crude oil scheduling cycle.

[0042] According to another optional embodiment of this application, the start time interval to the end time interval of multiple time intervals of the crude oil scheduling cycle are processed sequentially. If the crude oil scheduling cycle contains n time intervals, the first time interval, the second time interval, ..., the nth time interval are processed sequentially to obtain n target crude oil scheduling cycles. The n target crude oil scheduling cycles constitute the overall crude oil scheduling cycle.

[0043] In some optional embodiments of this application, the starting time interval to the ending time interval of multiple time intervals of the crude oil scheduling cycle are processed sequentially, including: in the process of processing multiple time intervals sequentially, a first time interval among the multiple time intervals is determined as a discrete variable, and a second time interval among the multiple time intervals is determined as a continuous variable, wherein the first time interval is any interval among the starting time interval to the ending time interval, and the second time interval is the time interval that is located after the first time interval and adjacent to the first time interval among the multiple time intervals.

[0044] In some optional embodiments of this application, before inputting the basic data and crude oil blending scheme into the target model, the method further includes: selecting target constraints from multiple constraints; and determining the target model based on the objective function and the target constraints.

[0045] As another optional implementation of this application, different enterprise users often have different concerns and needs regarding crude oil blending and scheduling. Users can establish personalized process models and select appropriate constraints to generate personalized crude oil scheduling models based on their actual business needs.

[0046] In an optional embodiment, before determining the target model based on the objective function and the objective constraints, the method further includes: converting nonlinear constraints among the multiple constraints into linear constraints.

[0047] According to an optional embodiment of this application, during the modeling process, to facilitate the subsequent solution process, the Big M method is used to transform nonlinear constraints into linear constraints, thereby transforming the problem from a mixed-integer nonlinear model into a mixed-integer linear model. The Big M method is a method for finding the initial basic feasible solution of a linear programming problem when the constraints are equal to or greater than a certain type, using the artificial variable method.

[0048] According to another optional embodiment of this application, the objective function is determined by the following parameters: the set of off-site terminal tanks, the set of on-site crude oil tanks, the set of atmospheric and vacuum distillation units, the set of time periods within the scheduling cycle, the inventory cost of terminal tanks, the feed switching cost of terminal tanks, the feed switching cost of atmospheric and vacuum distillation units, the number of switching times of terminal tanks within the time period, the number of switching times of atmospheric and vacuum distillation units within the time period, the average inventory of terminal tanks, and the average inventory of crude oil tanks; the objective constraints include at least one of the following: there is a corresponding terminal tank receiving oil when the ship delivers oil, the oil tanker is in material balance, the crude oil unloaded by the oil tanker is the same type as the crude oil that can be received by the terminal tank, the terminal tank cannot receive and deliver simultaneously, the upper and lower limits of terminal tank inventory, the average inventory of terminal tanks, the continuous oil receiving operation of a single terminal tank, and the deliverable oil volume of crude oil tanks.

[0049] As an optional embodiment of this application, the basic data also includes: the plan for oil tanker arrival at port, information on terminal tanks and crude oil tanks, the number of pipelines, the upper and lower limits of the processing capacity of the atmospheric and vacuum distillation unit, the crude oil blending scheme, and the unit switching cost. The information on the oil tanks includes at least: the upper and lower limits of tank storage, the initial inventory, and the inventory cost. The scheduling results also include: the scheduling arrangement for oil tankers to terminal tanks, the scheduling arrangement for terminal tanks to crude oil tanks, and the scheduling arrangement for crude oil tanks to atmospheric and vacuum distillation units.

[0050] Figure 2 This is a flowchart of another method for generating crude oil dispatch results according to an embodiment of this application, such as... Figure 2 As shown, the method includes the following steps:

[0051] Step S202, data structure design.

[0052] Step S20202: Input basic data into the optimization model (target model). The basic data includes: the plan for oil tanker arrival, information on terminal tanks and crude oil tanks (including upper and lower limits of tank storage, initial inventory, inventory costs, etc.), number of pipelines, upper and lower limits of atmospheric and vacuum distillation unit processing capacity, crude oil blending scheme, unit switching costs, etc.

[0053] In step S20204, the optimization model is continuously iterated and calculated.

[0054] Step S20206: Optimize the model output crude oil scheduling results, which include scheduling arrangements from oil tankers to terminal tanks, from terminal tanks to crude oil tanks, and from crude oil tanks to atmospheric and vacuum distillation units.

[0055] Step S204, generalized model construction.

[0056] Step S20402: Determine the objective function.

[0057]

[0058] Where d = {1, ..., ND} represents the set of off-site dock tanks, c = {1, ..., NC} represents the set of on-site crude oil tanks, cdu = {1, ..., NCDU} represents the set of atmospheric and vacuum distillation units, t = {1, ..., NT} represents the set of time periods, and C d,v C represents the inventory cost of the terminal tanks. c,v C represents the storage cost of crude oil in storage tanks. d,switch C represents the feed changeover cost of the terminal tank. cdu,switch This indicates the feed switching cost of the atmospheric and vacuum distillation unit. d,t Indicates the number of times the dock tanks switch within a time period. d,t A variable consisting of 0 and 1, where 0 represents no and 1 represents yes (the same applies below). Switch cdu,tIndicates the number of times the normal and low pressure systems switch within a time period. cdu,t V is a 0-1 variable. d,ave V represents the average inventory level of tanks at the dock. c,ave This indicates the average inventory level in crude oil tanks.

[0059] Step S20402: Determine the constraints.

[0060] Constraint 1: Each oil tanker may only unload oil once within the scheduling cycle.

[0061]

[0062] In the formula, s = {1, ..., NS} represents the set of oil tankers, d = {1, ..., ND} represents the set of dock tanks, and X s,d,t This indicates whether oil tanker s unloads oil into terminal tank d within time period t, and also indicates whether terminal tank d receives oil from oil tanker t within time period t. X s,d,t It is a 0-1 variable.

[0063] Constraint 2: Tanker Material Balance: Tanker ending oil quantity = beginning oil quantity - oil quantity shipped.

[0064]

[0065] In the formula, V s,t V represents the amount of oil a tanker carries during time period t. s,t-1 This indicates the amount of oil the tanker carried in the previous time period, t-1. s,d,t This represents the amount of oil unloaded by oil tanker s within time period t, and also the amount of oil received by terminal tank d within time period t.

[0066] Constraint 3: The crude oil unloaded by the oil tanker must be of the same type as the crude oil that can be received in the terminal tanks.

[0067] Oil s =Oil d

[0068] In the formula, Oil represents the type of crude oil. s Indicates the type of crude oil in the tanker. d This indicates the type of crude oil in the tanks at the dock.

[0069] Constraint 4: The feed flow rate of the terminal tank at each moment = feed flow stream (0-1) * (tanker loading capacity / tanker operation time),

[0070] F s,d,t =X s,d,t ×(V s / T s )

[0071] In the formula, F s,d,tX represents the amount of oil unloaded from oil tanker s within time period t, and also represents the amount of oil received by terminal tank d within time period t. s,d,t This indicates whether oil tanker s unloads oil into terminal tank d within time period t, and also whether terminal tank d receives oil from oil tanker t within time period t. V s Indicates the tanker's loading capacity, T s This indicates the time of operation for the oil tanker.

[0072] Constraint 5: Payment for tanks at the dock cannot be made on the same day as collection.

[0073] X s,d,t +X d,c,t ≤1

[0074] In the formula, X s,d,t This indicates whether oil tanker s unloads oil into terminal tank d within time period t, and also indicates whether terminal tank d receives oil from oil tanker t within time period t. X d,c,t X indicates whether, within time period t, terminal tank d delivers oil to crude oil tank c, and also indicates whether crude oil tank c receives oil from terminal tank d within the same time period. d,c,t It is a 0-1 variable.

[0075] Constraint 6: Only one share can be discharged from an oil tanker at any given time, and only one share can be deposited into a dockside tank at any given time.

[0076]

[0077] In the formula, X s,d,t This indicates whether oil tanker s unloads oil into dock tank d within time period t, and also indicates whether dock tank d receives oil from oil tanker t within time period t. s = {1,...,NS} represents the set of oil tankers, and d = {1,...,ND} represents the set of dock tanks.

[0078] Constraint 7: Upper and lower limits for tank storage at the dock.

[0079] V d,lb ≤V d ≤V d,ub

[0080] In the formula, V represents the tank inventory, d = {1, ..., ND} represents the set of dock tanks, lb represents the lower limit, and ub represents the upper limit.

[0081] Constraint 8: Material balance in dock tanks.

[0082]

[0083] In the formula, V d,t V represents the amount of oil in the terminal tanks during time period t. d,t-1 This represents the amount of oil in the terminal tanks during the previous time period, t-1. c = {1, ..., NC} represents the set of atmospheric and vacuum distillation units. F s,d,tF represents the amount of oil unloaded from oil tanker s within time period t, and also represents the amount of oil received by terminal tank d within time period t. d,c,t This represents the amount of oil unloaded from the terminal tank d within time period t, and also the amount of oil received by the atmospheric and vacuum distillation unit c within time period t.

[0084] Constraint 9: The amount of each type of oil that can be delivered from the dock tanks.

[0085] F d,c,t,oil ≤(V d -V d,lb )×Oil d,c,t

[0086] In the formula, F d,c,t,oil This represents the amount of each type of oil that terminal tank d can deliver to crude oil tank c within time period t. d,c,t This indicates the type of oil delivered from terminal tank d to crude oil tank c within time period t.

[0087] Constraint 10: Average inventory of tanks at the terminal.

[0088] V d,t,ave =0.5×(V) d,t +V d,t-1 )

[0089] In the formula, V d,t,ave This represents the average inventory level of the terminal tank d within the time period t.

[0090] Constraint 11: Oil receiving operations at a single terminal tank must be continuous.

[0091] Switch d,t ≥X s,d,t -X s,d,t-1

[0092] Constraint 12: Available oil volume in terminal tanks.

[0093] F d,c,t ≤(V d,t -V d,lb )×X d,c,t

[0094] Constraint 13: Type of oil supplied for tank loading at the dock.

[0095] Oil d,c,t ≥X d,c,t ×Oil d

[0096] Constraint 14: Payment for crude oil cannot be made while it is being collected from the tank.

[0097] X d,c,t +X c,cdu,t ≤1

[0098] In the formula, X c,cdu,tThis indicates whether crude oil tank c delivers oil to atmospheric and vacuum distillation unit cdu within time period t, and also whether atmospheric and vacuum distillation unit cdu receives oil from crude oil tank c within time period t. It is a 0-1 variable.

[0099] Constraint 15: Average inventory in crude oil tanks.

[0100] V c,t,ave =0.5×(V) c,t +V c,t-1 )

[0101] Constraint 16: Lower limit of blended crude oil ratio <= Blended crude oil ratio <= Upper limit of blended crude oil ratio

[0102] R mix,lb ≤R mix ≤R mix,ub

[0103] In the formula, R represents the proportion of crude oil.

[0104] Constraint 17: Crude oil tank material balance

[0105]

[0106] Constraint 18: Available oil volume in crude oil tanks.

[0107] F c,cdu,t ≤(V c,t -V c,lb )×X c,cdu,t

[0108] Constraint 19: The total amount of crude oil discharged from all crude oil tanks at all times is greater than or equal to the lower limit of atmospheric and vacuum distillation processing capacity and less than or equal to the upper limit of atmospheric and vacuum distillation processing capacity.

[0109]

[0110] In the formula, Process represents the atmospheric and vacuum distillation process.

[0111] Constraint 20: Number of atmospheric and vacuum feed streams.

[0112] X c,cdu,t ≤1

[0113] Constraint 21: Whether to switch to atmospheric and vacuum feed,

[0114] Switch cdu,t ≥X c,cdu,t -X c,cdu,t-1

[0115] Step S206: Personalized model generation.

[0116] Different enterprise users often have different concerns and needs regarding crude oil blending and scheduling. Users can build personalized process models based on their actual business needs, select appropriate constraints (such as calculating scheduling time intervals, crude oil blending schemes, etc.), and generate personalized crude oil scheduling models.

[0117] Step S208, crude oil scheduling optimization calculation, the schematic diagram of the crude oil scheduling optimization calculation process is as follows: Figure 3 As shown:

[0118] (1) Input initial information, including the arrival plan of the oil tanker, the inventory of the terminal tank, the inventory of the oil tank, the processing volume of atmospheric and vacuum distillation, the start and end time of the scheduling, the time interval, etc.

[0119] (2) Input the crude oil blending scheme, that is, the crude oil blending ratio for each time period within the scheduling cycle.

[0120] (3) Establish relevant variables, objective functions, and constraints. During the modeling process, to facilitate subsequent solutions, the Big M method is adopted to transform constraints 3, 9, 12, and 18 in step S20402 from nonlinear to linear, thereby transforming the problem from a mixed-integer nonlinear model to a mixed-integer linear model. The model uses a synchronous time period to ensure all devices have the same time axis division, avoiding the generation of local optima.

[0121] (4) Solve the model established in the previous step. If the solution is successful, output the result; if the solution fails, repeat the solution process. The process of repeating the solution is as follows: Figure 4 As shown:

[0122] Crude oil scheduling is carried out in chronological order within a custom scheduling cycle. Since time intervals are discrete variables, the entire scheduling cycle is divided into many time intervals. In chronological order, the first time interval is fixed as a discrete variable, and the time intervals after relaxation are continuous variables. After optimization, the first time interval is fixed as the optimization result. Then, the second time interval is fixed as a discrete variable, and the time intervals after the second time interval are relaxed as continuous variables. After optimization, the second time interval is fixed as the optimization result, and so on.

[0123] (5) Output the solution results.

[0124] In the above steps, the applicability of the crude oil dispatching model in industry is improved by modeling the entire process of crude oil from oil tankers to off-site terminal tanks to crude oil tanks in the plant area and then to the atmospheric and vacuum distillation unit.

[0125] According to an optional embodiment of this application, firstly, the blending ratio of each batch of crude oil, the zoning of terminal tanks, the tank inventory of terminal tanks and crude oil tanks, the crude oil supply plan (shipment schedule, quantity, and properties), and the crude oil processing plan are known. Secondly, based on the known conditions and considering the constraints, an overall crude oil scheduling model is established by minimizing the objective function. Finally, the crude oil production scheduling table from oil tankers to atmospheric and vacuum distillation units is obtained. The constraints of the crude oil scheduling model contain many mixed integer problems. These problems can be gradually degenerated into linear problems and solved using branch and bound methods, genetic algorithms, or finite rules. The established overall optimization strategy for crude oil scheduling can help refineries formulate long-term crude oil scheduling plans, reduce the workload of scheduling personnel, and ensure the operability of the crude oil scheduling plan.

[0126] According to another optional embodiment of this application, the crude oil dispatching process of a certain plant is as follows: Figure 5 As shown, the system includes oil tankers, terminal tanks, crude oil tanks, and atmospheric and vacuum distillation units. The arrival schedule of the oil tankers, the initial and upper / lower limits of the terminal tanks and crude oil tanks, the processing capacity of the atmospheric and vacuum distillation units, and the crude oil blending scheme are known. Using the method described in this application, the overall crude oil scheduling model is optimized, resulting in the following crude oil scheduling table.

[0127]

[0128]

[0129] Figure 6 This is a structural diagram of a crude oil dispatch result generation device according to an embodiment of this application, such as... Figure 6 As shown, the device includes:

[0130] The solver module 60 is used to solve the target model based on the basic data and crude oil ratio information to obtain the crude oil scheduling result. The target model is used to indicate the relationship between the basic data, crude oil ratio information and crude oil scheduling result. The basic data includes at least one of the following: crude oil scheduling cycle, oil tanker unloading, crude oil transfer and crude oil processing flow. The crude oil ratio information is the crude oil ratio of multiple time intervals of the crude oil scheduling cycle. The crude oil scheduling result includes at least the oil tanker and terminal tanker oil receipt and payment scheduling arrangements in each time interval.

[0131] The first generation module 62 is used to process multiple time intervals of the crude oil scheduling cycle when the target model fails to be solved.

[0132] The second generation module 64 is used to input the processed time interval into the target model to generate the target crude oil scheduling result.

[0133] It should be noted that the above Figure 6Each module can be a program module (e.g., a set of program instructions that implements a specific function) or a hardware module. For the latter, it can take the following forms, but is not limited to them: each of the above modules is represented by a processor, or the functions of each of the above modules are implemented by a processor.

[0134] Figure 7 A hardware block diagram of a computer terminal (or mobile device) for generating crude oil dispatch results is shown. Figure 7 As shown, a computer terminal 70 (or mobile device 70) may include one or more processors 702 (shown as 702a, 702b, ..., 702n in the figure) 702 (processor 702 may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.), a memory 704 for storing data, and a transmission module 706 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 7 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, the computer terminal 70 may also include... Figure 7 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0135] It should be noted that the aforementioned one or more processors 702 and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer terminal 70 (or mobile device). As involved in the embodiments of this application, the data processing circuits serve as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0136] The memory 704 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the crude oil scheduling result generation method in this embodiment. The processor 702 executes various functional applications and data processing by running the software programs and modules stored in the memory 704, thereby implementing the above-mentioned application vulnerability detection method. The memory 704 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 704 may further include memory remotely located relative to the processor 702, and these remote memories can be connected to the computer terminal 70 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0137] The transmission device 706 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the computer terminal 70. In one example, the transmission device 706 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 706 may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0138] The display can be, for example, a touchscreen liquid crystal display (LCD) that allows the user to interact with the user interface of the computer terminal 70 (or mobile device).

[0139] It should be noted here that, in some optional embodiments, the above... Figure 7 The computer device (or electronic device) shown may include hardware elements (including circuitry), software elements (including computer code stored on a computer-readable medium), or a combination of both hardware and software elements. It should be noted that... Figure 7 This is only one instance of a particular specific instance, and is intended to illustrate the types of components that may exist in the aforementioned computer equipment (or electronic equipment).

[0140] It should be noted that, Figure 7 The electronic device shown is used to perform Figure 1 The method for generating crude oil dispatch results shown above also applies to this electronic device, and will not be repeated here.

[0141] This application also provides a non-volatile storage medium, which includes a stored program, wherein the program, when running, controls the device where the storage medium is located to execute the above-mentioned crude oil scheduling result generation method.

[0142] A non-volatile storage medium performs the following functions: Solving a target model based on basic data and crude oil blending information to obtain crude oil scheduling results. The target model indicates the relationship between the basic data, crude oil blending information, and crude oil scheduling results. The basic data includes at least one of the following: crude oil scheduling cycle, tanker unloading, crude oil transfer, and crude oil processing procedures. The crude oil blending information is the crude oil blending ratio for multiple time intervals within the crude oil scheduling cycle. The crude oil scheduling results include at least the oil tanker and terminal tanker oil receipt and payment scheduling arrangements for each time interval. If the target model fails to solve, the multiple time intervals of the crude oil scheduling cycle are processed to generate multiple target crude oil scheduling cycles. The target crude oil scheduling cycles are then input into the target model to generate target crude oil scheduling results.

[0143] This application also provides an electronic device, including: a memory and a processor, wherein the processor is used to run a program stored in the memory, wherein the program executes the above-described crude oil scheduling result generation method during runtime.

[0144] The processor is used to run programs that perform the following functions: Solving the target model based on basic data and crude oil blending information to obtain crude oil scheduling results. The target model indicates the relationship between the basic data, crude oil blending information, and crude oil scheduling results. The basic data includes at least one of the following: crude oil scheduling cycle, oil tanker unloading, crude oil transfer, and crude oil processing procedures. The crude oil blending information is the crude oil blending ratio for multiple time intervals within the crude oil scheduling cycle. The crude oil scheduling results include at least the oil tanker and terminal tanker oil receipt and payment scheduling arrangements for each time interval. If the target model fails to solve, the processor processes multiple time intervals of the crude oil scheduling cycle to generate multiple target crude oil scheduling cycles. The processor then inputs the target crude oil scheduling cycles into the target model to generate target crude oil scheduling results.

[0145] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0146] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0147] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0148] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0149] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0150] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to related technologies, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0151] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for generating crude oil dispatch results, characterized in that, include: The target model is solved based on the basic data and crude oil blending information to obtain the crude oil scheduling result. The target model is used to indicate the relationship between the basic data, the crude oil blending information and the crude oil scheduling result. The basic data includes at least one of the following: crude oil scheduling cycle, oil tanker unloading, crude oil transfer and crude oil processing flow. The crude oil blending information is the crude oil blending ratio of multiple time intervals of the crude oil scheduling cycle. The crude oil scheduling result includes at least the oil tanker and terminal tanker oil receipt and payment scheduling arrangements in each of the time intervals. If the target model fails to be solved, multiple time intervals of the crude oil scheduling cycle are processed to generate multiple target crude oil scheduling cycles. Input the target crude oil scheduling cycle into the target model to generate the target crude oil scheduling result; Processing multiple time intervals of the crude oil scheduling cycle includes: In the process of processing multiple time intervals sequentially, the first time interval among the multiple time intervals is determined as a discrete variable, and the second time interval among the multiple time intervals is determined as a continuous variable. The first time interval is any interval from the start time interval to the end time interval, and the second time interval is the time interval that is located after the first time interval and adjacent to the first time interval among the multiple time intervals.

2. The method of claim 1, wherein, Before inputting the basic data and crude oil blending scheme into the target model, the method further includes: Select the target constraint from multiple constraints; The target model is determined based on the objective function and the objective constraints.

3. The method according to claim 2, characterized in that, Before determining the target model based on the objective function and the objective constraints, the method further includes: The nonlinear constraints among the multiple constraints are transformed into linear constraints.

4. The method according to claim 2, characterized in that, The objective function is determined by the following parameters: the set of off-site terminal tanks, the set of on-site crude oil tanks, the set of atmospheric and vacuum distillation units, the set of time periods within the scheduling cycle, the inventory cost of the terminal tanks, the feed switching cost of the terminal tanks, the feed switching cost of the atmospheric and vacuum distillation units, the number of switching times of the terminal tanks within the time period, the number of switching times of the atmospheric and vacuum distillation units within the time period, the average inventory level of the terminal tanks, and the average inventory level of the crude oil tanks. The target constraints include at least one of the following: each oil tanker can only unload oil once during the crude oil dispatch cycle; oil tanker material balance; the crude oil unloaded by the oil tanker is consistent with the type of crude oil that can be received by the terminal tanks; the upper and lower limits of terminal tank inventory; the average inventory of terminal tanks; the continuous oil receiving operation of a single terminal tank; and the amount of crude oil that can be delivered by the crude oil tanks.

5. The method according to claim 1, characterized in that, The basic data also includes: the tanker arrival schedule, information on terminal tanks and crude oil tanks, number of pipelines, upper and lower limits of atmospheric and vacuum distillation unit processing capacity, crude oil blending scheme and unit switching costs. Among them, the information on the oil tanks includes at least: upper and lower limits of tank storage, initial inventory and inventory cost. The crude oil dispatch results also include: the dispatch arrangements from oil tankers to terminal tanks, the dispatch arrangements from terminal tanks to crude oil tanks, and the dispatch arrangements from crude oil tanks to atmospheric and vacuum distillation units.

6. A crude oil dispatch result generation device, characterized in that, include: The solution module is used to solve the target model based on the basic data and crude oil blending information to obtain the crude oil scheduling result. The target model is used to indicate the relationship between the basic data, the crude oil blending information and the crude oil scheduling result. The basic data includes at least one of the following: crude oil scheduling cycle, oil tanker unloading, crude oil transfer and crude oil processing flow. The crude oil blending information is the crude oil blending ratio of multiple time intervals of the crude oil scheduling cycle. The crude oil scheduling result includes at least the oil tanker and terminal tanker oil receipt and payment scheduling arrangements in each of the time intervals. The first generation module is used to process multiple time intervals of the crude oil scheduling cycle to generate multiple target crude oil scheduling cycles when the target model fails to solve. In the process of processing the multiple time intervals in sequence, the first time interval among the multiple time intervals is determined as a discrete variable, and the second time interval among the multiple time intervals is determined as a continuous variable. The first time interval is any interval from the start time interval to the end time interval, and the second time interval is the time interval that is after the first time interval and adjacent to the first time interval among the multiple time intervals. The second generation module is used to input the target crude oil scheduling cycle into the target model and generate the target crude oil scheduling result.

7. A non-volatile storage medium, characterized in that, The non-volatile storage medium includes a stored program, wherein, when the program is running, it controls the device containing the non-volatile storage medium to execute the crude oil scheduling result generation method according to any one of claims 1 to 5.

8. An electronic device, comprising: include: A memory and a processor, the processor being configured to run a program stored in the memory, wherein the program, when running, executes the crude oil scheduling result generation method according to any one of claims 1 to 5.

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