Planned shutdown scheduling scheme optimization method for refining and petrochemical production device

Abstract

A planned shutdown scheduling scheme optimization method for a refining and petrochemical production device. The method comprises: first, collecting production device information and material inventory information on the basis of an actual production process of a refinery, and then constructing a basic scheduling model in a normal operation state of the refinery on the basis of the collected production device information and material inventory information; then solving the basic scheduling model on the basis of processing device information and the material inventory information, so as to acquire a load reference value, a material flow reference value and a material inventory reference value of each production device; next, on the basis of the load reference value, the material flow reference value and the material inventory reference value of each production device obtained by means of solving, in combination with a refinery planned shutdown arrangement, constructing a production device planned shutdown scheduling optimization model; and finally, solving the production device planned shutdown scheduling optimization model to generate a planned shutdown scheduling scheme, thereby completing scheduling scheme optimization of production devices in a planned shutdown state.

Description

An optimization method for scheduling planned shutdowns of refining and chemical production units Invention Field

[0001] This invention relates to the field of refinery production scheduling, and more specifically to an optimization method for scheduling planned shutdowns of refining and chemical production units. Background Technology

[0002] The oil refining industry is one of the important pillar industries of the national economy, bearing the crucial responsibility of converting crude oil into various petroleum products. As the core facilities of the oil refining industry, the efficient and safe operation of oil refineries plays an irreplaceable role in ensuring energy supply and promoting economic development. The production process of an oil refinery typically includes two main stages: primary processing and secondary processing. Secondary processing refers to the further processing of intermediate products from primary processing to obtain higher-quality petroleum products. These secondary units encompass a series of complex chemical reaction processes such as catalytic cracking, hydrocracking, delayed coking, and reforming. They are not only highly adaptable to different feedstocks but also capable of producing a variety of high-value-added products such as gasoline, diesel, and aviation kerosene to meet the needs of different markets and users.

[0003] However, the operation of secondary units in refineries is not static. Due to factors such as fluctuations in market demand, changes in feedstock quality, and equipment maintenance needs, refineries require periodic shutdowns and maintenance of secondary units to ensure long-term stable operation and maximize production efficiency. Planned shutdowns for maintenance and troubleshooting are a systematic project involving multiple aspects such as orderly unit shutdowns, material balancing, and inventory management, significantly impacting the overall operational plan and economic benefits of the refinery. Traditional shutdown scheduling often relies on manual decisions by experienced operators. While this method is intuitive, it often struggles to balance efficiency, cost, and safety when dealing with complex and ever-changing production situations, easily leading to resource waste, environmental pollution, and safety hazards.

[0004] In recent years, with the continuous improvement of industrial automation and informatization, refinery production management is gradually developing towards intelligence and precision. Advanced modeling and optimization technologies, such as linear programming, nonlinear programming, mixed integer programming, and intelligent algorithms, have provided new solutions for refinery shutdown scheduling. By constructing mathematical models and comprehensively considering multi-objective constraints such as material balance, energy utilization, environmental impact, and time scheduling, and optimizing key decision variables such as shutdown sequence, material handling strategies, and equipment maintenance plans, the efficiency and effectiveness of revising the entire process production scheduling plan due to planned shutdowns for maintenance and troubleshooting can be significantly improved, reducing unnecessary downtime and costs, and enhancing the overall competitiveness of refineries. Therefore, developing a more accurate, efficient, and intelligent shutdown scheduling model and optimization algorithm has become a key issue that the refining industry urgently needs to address.

[0005] Invention Overview

[0006] The following provides a brief overview of one or more aspects to offer a basic understanding of them. This overview is not an exhaustive summary of all conceived aspects, nor is it intended to identify key or decisive elements of 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 to prepare for the more detailed descriptions that follow.

[0007] The purpose of this invention is to solve the above-mentioned problems and provide an optimization method for the planned shutdown scheduling of refining and chemical production units. This method addresses the issues of low efficiency and poor effectiveness in unit scheduling during refining and chemical shutdown events due to reliance on manual experience, which provides a strong scientific basis and technical support for planned shutdown scheduling in oil refineries.

[0008] The technical solution of this invention is as follows:

[0009] This invention provides a method for optimizing the scheduling of planned shutdowns of refining and chemical production units, comprising the following steps:

[0010] Step S1: Collect information on production equipment and material inventory based on the actual production process of the refinery;

[0011] Step S2: Construct a basic scheduling model for the refinery under normal operating conditions based on the collected production unit information and material inventory information;

[0012] Step S3: Solve the basic scheduling model based on the processing device information and material inventory information to obtain the load baseline value, material flow baseline value and material inventory baseline value of each production device;

[0013] Step S4: Based on the load baseline value, material flow baseline value, and material inventory baseline value of each production unit obtained by solving, and in conjunction with the refinery's planned shutdown arrangements, construct a planned shutdown scheduling optimization model for the production units.

[0014] Step S5: Solve the planned shutdown scheduling optimization model of the production unit to generate a planned shutdown scheduling scheme, thereby completing the optimization of the production unit scheduling scheme under the planned shutdown state.

[0015] According to an embodiment of the attribute-based optimization method for planned shutdown scheduling of refining and chemical production units based on the present invention, the production unit information includes information on atmospheric and vacuum distillation units and secondary processing units involved in production, and the material inventory information includes raw material information, intermediate material information, and product information. The planned shutdown scheduling scheme for refining and chemical production units constructs a basic scheduling model simulating the normal operating state of a refinery based on the collected production unit information and material inventory information.

[0016] According to an embodiment of the attribute-based optimization method for planned shutdown scheduling of refining and chemical production units of the present invention, after obtaining production unit information and material inventory information, the method establishes constraints for a basic scheduling model based on the obtained production unit information; wherein, the constraints for the basic scheduling model include: unit processing capacity constraints, unit material balance constraints, inventory inflow and outflow balance constraints, inventory upper and lower limits constraints, raw material supply constraints, and unit feed ratio constraints.

[0017] According to an embodiment of the attribute-based optimization method for planned shutdown scheduling of refining and chemical production units based on the present invention, the unit processing capacity constraints are as follows:

[0018] Where U represents the set of production units,

[0019] Cap u,i This represents the load of production unit u on day i.

[0020] This indicates the upper limit of the load of production unit u.

[0021] This indicates the lower limit of the load of production unit u.

[0022] According to an embodiment of the attribute-based optimization method for planned shutdown scheduling of refining and chemical production units based on the present invention, and according to claim 4, the optimization method for planned shutdown scheduling of refining and chemical production units is characterized in that, when constructing material balance constraints, the optimization method for planned shutdown scheduling of refining and chemical production units constructs a material balance model based on material receipt and payment and the material flow relationship between processing units; wherein, the material balance model includes material flow analysis and material supply and demand matching strategy; when the basic scheduling model fails to solve, the optimization method for planned shutdown scheduling of refining and chemical production units applies unit material balance constraints to the basic scheduling model based on the material balance model, thereby adjusting the initial parameters of the material balance model to obtain a feasible solution while ensuring that the material inventory remains unchanged during the scheduling cycle.

[0023] According to an embodiment of the attribute-based optimization method for planned shutdown scheduling of refining and chemical production units of the present invention, the material balance constraints include secondary unit material balance constraints and diversion unit material balance constraints. The optimization method for planned shutdown scheduling of refining and chemical production units uses a material balance model to apply secondary unit material balance constraints and diversion unit material balance constraints to the basic scheduling model, thereby adjusting the initial parameters of the material balance model while keeping the material inventory unchanged during the scheduling cycle.

[0024] According to an embodiment of the attribute-based optimization method for planned shutdown scheduling of refining and chemical production units based on the present invention, the material balance constraints of the secondary unit are as follows:

[0025] Where U represents the set of production units,

[0026] Cap u,i This represents the production load of production unit u on day i.

[0027] yield u,s This indicates the yield of each output stream s of the production unit u.

[0028] Mass s,i This represents the flow rate of each stream s in production unit u on day i.

[0029] According to an embodiment of the attribute-based optimization method for planned shutdown scheduling of refining and chemical production units based on the present invention, the material balance constraints of the diversion unit are as follows:

[0030] Where U represents the set of production units,

[0031] U SPL This represents a collection of diversion devices.

[0032] This indicates the feed collection of the diversion device.

[0033] This indicates the collection of materials discharged from the diversion device.

[0034] Mass s,i This represents the flow rate of each stream s in the diversion device on day i.

[0035] According to an embodiment of the attribute-based optimization method for planned shutdown scheduling of refining and chemical production units based on the present invention, the inventory inflow and outflow balance constraints are as follows:

[0036] Among them, Inv t,i This represents the inventory level of inventory t on day i.

[0037] Collection of incoming materials from inventory.

[0038] This represents the set of materials shipped from inventory.

[0039] Inv t,i This represents the liquid level of inventory t on day i.

[0040] Inv t,i-1 This represents the liquid level t in the inventory on day i-1.

[0041] According to an embodiment of the attribute-based optimization method for planned shutdown scheduling of refining and chemical production units based on the present invention, the upper and lower inventory limits are constrained as follows:

[0042] in, This represents the upper limit of the can storage capacity of inventory t.

[0043] This represents the lower limit of the tank storage capacity for inventory t.

[0044] According to an embodiment of the attribute-based optimization method for planned shutdown scheduling of refining and chemical production units based on the present invention, after determining the constraints of the basic scheduling model, the method solves the basic scheduling model with the minimum load fluctuation of each production unit as the optimization objective and the constant material inventory as a special constraint, thereby obtaining the load benchmark value, material flow benchmark value, and material inventory benchmark value of each production unit; wherein, the optimization objective is as follows:

[0045] Where Obj represents the optimization objective value of the basic scheduling model.

[0046] Cap u,i Let represent the production load of production unit u on day i.

[0047] Cap u,0 This indicates the set value of the device load.

[0048] λ1 represents the weighting coefficient for the load deviation of the equipment.

[0049] μ1 represents the device load switching penalty coefficient.

[0050] N u This indicates the number of times the device load is switched.

[0051] According to an embodiment of the attribute-based optimization method for planned shutdown scheduling of refining and chemical production units based on the present invention, the method introduces a binary variable ∈ of the unit shutdown state when constructing the planned shutdown scheduling optimization model for the production unit. u The daily change in inventory is included in the state binary variable p. t To constrain; where,

[0052] If the binary variable representing the shutdown state of the device ∈ u A value of "1" indicates that production unit u is running, while a value of "0" indicates that production unit u is in a planned shutdown state.

[0053] If the daily change in inventory is included in the binary variable p of state t A value of "1" indicates that the daily change in inventory t is included, while a value of "0" indicates that the daily change in inventory t is not included.

[0054] According to an embodiment of the attribute-based optimization method for planned shutdown scheduling of refining and chemical production units based on the present invention, the optimization model for planned shutdown scheduling of production units has multiple optimization objectives, including: minimizing the number of unit load adjustments, minimizing the change in inventory at the beginning and end of the period, minimizing the daily change in inventory, and minimizing the unit shutdown duration; wherein, when solving the optimization model for planned shutdown scheduling of refining and chemical production units, the optimization method sets corresponding weight coefficients according to the importance of each optimization objective, and transforms multiple objectives into a single objective for optimization through coefficient weighting, thereby obtaining the final solution of the optimization model for planned shutdown scheduling of production units.

[0055] According to an embodiment of the attribute-based optimization method for planned shutdown scheduling of refining and chemical production units based on the present invention, the optimization objective of the planned shutdown scheduling optimization model for production units is as follows:

[0056] Where Obj represents the optimization objective value of the planned shutdown scheduling optimization model for production units.

[0057] Cap u,i Cap u,i This represents the load of production unit u on day i.

[0058] Cap u,0 Cap u,i This represents the initial load value of production unit u.

[0059] N u Indicates the number of times the device load is switched.

[0060] ∈ uThe binary variable represents the shutdown status of the equipment.

[0061] Inv t,i This represents the liquid level of inventory t on day i.

[0062] Int t,0 This represents the initial liquid level of the inventory on day i-1.

[0063] Let represent the maximum liquid level of inventory t on day i.

[0064] μ2 represents a shutdown penalty coefficient.

[0065] λ1, μ1, λ2, and λ3 are the weighting coefficients of the corresponding parameters.

[0066] The present invention also provides a computer-readable medium storing computer program code that, when executed by a processor, implements the method described above.

[0067] This invention also provides a method and apparatus for optimizing the scheduling of planned shutdowns of refining and chemical production units, comprising:

[0068] Memory, used to store instructions that can be executed by a processor; and

[0069] A processor for executing the instructions to implement the method described above.

[0070] Compared with existing technologies, this invention offers the following advantages: For planned shutdowns of refining and chemical production units, this invention uses the scheduling scheme obtained from the basic scheduling model under normal refinery operation as a benchmark to construct an intelligent production unit planned shutdown scheduling optimization model, automatically optimizing the scheduling scheme for production units under planned shutdown conditions. This invention overcomes the limitations of traditional scheduling methods that rely on manual experience, significantly improving the efficiency and accuracy of scheduling decisions. This allows refineries to quickly respond to market changes and production demands, automatically identifying the optimal shutdown and restart strategies, greatly shortening unplanned shutdown time and reducing workload. Simultaneously, this invention enables refined management of inventory, material balance, and unit load, effectively reducing additional costs caused by shutdowns, such as raw material waste and increased energy consumption. Through preventative maintenance and advance planning, it reduces equipment failure rates and the risk of secondary shutdowns, ensuring production safety and stability. Furthermore, this invention specifically considers actual operating conditions, aiming to reduce the number of unit switching operations and avoid energy waste and equipment wear caused by frequent start-ups and shutdowns. By optimizing equipment load adjustment and material flow, a smooth transition in the production process is ensured, reducing operational difficulty and improving the overall stability of the production system and the work efficiency of operators. Attached Figure Description

[0071] The above-described features and advantages of the present invention will be better understood after reading the following detailed description of embodiments of the present disclosure in conjunction with the accompanying drawings. In the drawings, components are not necessarily drawn to scale, and components having similar related characteristics or features may have the same or similar reference numerals.

[0072] Figure 1 is a flowchart illustrating an embodiment of the attribute-based optimization method for planned shutdown scheduling of refining and chemical production units according to the present invention.

[0073] Detailed description of the invention

[0074] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this application. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0075] As indicated in this application and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" are not specifically singular and may include plural forms. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of explicitly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0076] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of this application. It should also be understood that, for ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale. Techniques, methods, and devices known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and devices should be considered part of the specification. In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters in the following drawings denote similar items; therefore, once an item is defined in one drawing, it need not be further discussed in subsequent drawings.

[0077] In describing the embodiments of the present invention in detail, for ease of explanation, the cross-sectional views illustrating the device structure will be partially enlarged and not to scale. Furthermore, the schematic diagrams are merely examples and should not be construed as limiting the scope of protection of the present invention. In actual fabrication, the three-dimensional spatial dimensions of length, width, and depth should be included.

[0078] In the description of this application, it should be understood that the orientation or positional relationship indicated by directional terms such as "front, back, up, down, left, right", "horizontal, vertical, horizontal" and "top, bottom" is usually based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing this application and simplifying the description. Unless otherwise stated, these directional terms do not indicate or imply that the device or element referred to must have a specific orientation or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on the scope of protection of this application; the directional terms "inner" and "outer" refer to the inner and outer contours relative to the outline of each component itself.

[0079] This document discloses an embodiment of an attribute-based optimization method for planned shutdown scheduling of refining and chemical production units. Figure 1 is a flowchart illustrating an embodiment of this attribute-based optimization method for planned shutdown scheduling of refining and chemical production units. Referring to Figure 1, the following is a detailed description of each step of the attribute-based optimization method for planned shutdown scheduling of refining and chemical production units.

[0080] Step S1: Collect information on production equipment and material inventory based on the actual production process of the refinery.

[0081] Step S2: Construct a basic scheduling model for the refinery under normal operating conditions based on the collected production unit information and material inventory information.

[0082] In this embodiment, the production unit information includes information on the atmospheric and vacuum distillation unit and the secondary processing unit involved in production. Specifically, it involves detailed information on the atmospheric and vacuum distillation unit, the secondary processing unit, raw materials, intermediate materials, and products. The material inventory information includes raw material information, intermediate material information, and product information. Based on the production unit information and material inventory information collected from the actual production process of the refinery, a basic scheduling model simulating the normal operation of the refinery is constructed.

[0083] Specifically, in this embodiment, the production unit comprises two or more atmospheric and vacuum distillation units and multiple secondary processing units, including but not limited to catalytic cracking, delayed coking, residue hydrotreating, hydrocracking, reforming pre-hydrotreating, continuous reforming, gasoline and diesel hydrotreating, S-zorb, slurry topping, light hydrocarbon recovery, solvent deasphalting, alkylation, aromatics complex, No. 1 gas separator, and No. 2 gas separator. Inventory considerations include raw materials, products, and some intermediate materials, including but not limited to aviation kerosene, hydrotreating feedstock, light waste oil, wax oil, hydrotreating tail oil, heavy residue oil, residue oil, reforming feedstock, slurry oil, gas separator feedstock, catalytic gasoline, commercial gasoline, commercial diesel, aviation kerosene products, paraxylene products, liquefied petroleum gas products, asphalt products, deoiled asphalt, coking wax oil, light hydrocarbon recovery naphtha, refined oil, reforming product oil, and purchased xylene. Based on this production unit information and material inventory information, a basic scheduling model simulating the normal operation of a refinery can be constructed.

[0084] Step S3: Solve the basic scheduling model based on the processing device information and material inventory information to obtain the load baseline value, material flow baseline value and material inventory baseline value of each production device.

[0085] In this embodiment, before solving the basic scheduling model, it is necessary to establish constraints on the basic scheduling model based on the obtained production unit information. These constraints include: unit processing capacity constraints, unit material balance constraints, inventory inflow / outflow balance constraints, inventory upper and lower limits constraints, raw material supply constraints, and unit feed ratio constraints. When solving the basic scheduling model, these constraints are used to ensure that the resulting scheduling scheme conforms to the actual production process.

[0086] Specifically, in this embodiment, when setting the processing capacity constraints of the device, considering the need to constrain the load on the production device, the following constraints were set:

[0087] Where U represents the set of production units, Cap u,i This represents the load of production unit u on day i. This indicates the upper limit of the load of production unit u. This indicates the lower limit of the load of production unit u. In actual production, considering the actual production situation, the upper limit of the operating load of the production unit can be set to 110% of the design load, and the lower limit of the operating load can be set to 70%. The upper and lower limits of inventory are set according to the actual capacity of the storage tanks, and the initial inventory of each material can be set.

[0088] In this embodiment, when constructing material balance constraints, the first step is to organize the material receipt and payment and the material flow relationships between processing devices based on the actual production process. This ensures that every link in the entire production process is closely connected through material flow, forming a coherent material balance model. The material balance model includes material flow analysis and material supply and demand matching strategies. When the basic scheduling model fails to solve, device material balance constraints can be applied to the basic scheduling model based on the material balance model. This allows for adjusting the initial parameters of the material balance model while maintaining constant material inventory within the scheduling cycle, thereby obtaining a feasible solution.

[0089] Specifically, in this embodiment, material balance constraints include secondary unit material balance constraints and diversion unit material balance constraints. When the basic model cannot obtain a feasible solution, further adjustments to the basic scheduling model for the entire process material balance are required. Under the secondary unit material balance constraints and diversion unit material balance constraints, material flow analysis and material supply and demand matching strategies are used to adjust the initial parameters of the basic model, thereby ensuring that the material inventory remains unchanged during the scheduling cycle to achieve a dynamic balance between material supply and demand throughout the entire process. If the inventory of a certain material decreases, the output of that material can be increased by increasing the load of upstream units, or the demand for that material can be reduced by decreasing the load of downstream units or reducing product sales, in order to achieve a supply and demand balance.

[0090] The material balance constraints for the secondary unit are as follows:

[0091] Where U represents the set of production units, Cap u,i This represents the production load of production unit u on day i, yield. u,s Mass represents the yield of each output stream s of the production unit u. s,i This represents the flow rate of each stream s in production unit u on day i.

[0092] The material balance constraints for the diversion device are as follows:

[0093] Where U represents the set of production units, U SPL This represents a collection of diversion devices. This indicates the feed collection of the diversion device. Mass represents the combined feed and discharge of the diversion device. s,i This represents the flow rate of each stream s in the diversion device on day i.

[0094] In this embodiment, when setting the inventory inflow and outflow balance constraint, it is necessary to consider the inventory inflow and outflow situations, and the constraint conditions are as follows:

[0095] Among them, Invt,i This represents the inventory level of inventory t on day i. This represents the collection of incoming materials from inventory. Inv represents the collection of inventory outputs. t,i Inv represents the liquid level of inventory t on day i. t,i-1 This represents the liquid level t in the inventory on day i-1.

[0096] When setting upper and lower limits for inventory, the tank storage capacity corresponding to different inventories is considered, and the constraints are as follows:

[0097] in, This represents the upper limit of the can storage capacity of inventory t. This indicates the lower limit of the tank storage capacity for inventory t. In one embodiment, the production equipment and material inventory may be subject to design and customization constraints as shown below, including but not limited to: the proportion of slurry oil and deoiled bitumen in the feed of the delayed coking unit not exceeding 20%; the slag blending ratio in the feed of the residue hydrotreating unit being 50%-55%; the proportion of residue plus heavy oil in the feed of the catalytic cracking unit not exceeding 70%; the proportion of purchased xylene in the feed of the aromatics complex not exceeding 50%; straight-run diesel oil may consist of atmospheric and vacuum distillation unit line 1 and line 2; and straight-run wax oil may consist of atmospheric and vacuum distillation unit line 3 and lines 1, 2, and 3, etc.

[0098] In this embodiment, after determining the constraints of the basic scheduling model, the basic scheduling model is solved with the optimization objective of minimizing load fluctuations of each production unit and the special constraint of maintaining constant material inventory. The resulting scheduling scheme is used as the benchmark for the planned shutdown scheduling optimization model of the production units, thereby obtaining the load benchmark value, material flow benchmark value, and material inventory benchmark value for each production unit. The optimization objective of the basic scheduling model is as follows:

[0099] Where Obj represents the target value for optimization of the basic scheduling model, and Cap u,i Cap represents the production load of production unit u on day i. u,0 The set value N represents the load of the device. u λ1 represents the number of load switching operations, μ1 represents the load deviation weighting coefficient, and μ1 represents the load switching penalty coefficient.

[0100] Furthermore, in this embodiment, some special constraints are involved in solving the basic scheduling model, including but not limited to: all units are operating normally, no inventory is used, there are no product demand restrictions, product demand is considered unrestricted, and all products are sold according to actual output. Combining these special constraints to optimize the basic scheduling model yields the optimal operating point. Under normal operating conditions, the refinery's production process remains at this baseline state, providing a foundation for subsequent optimization models of planned unit shutdown scheduling.

[0101] Step S4: Based on the load baseline value, material flow baseline value, and material inventory baseline value of each production unit obtained by solving, and in conjunction with the refinery's planned shutdown arrangements, construct a planned shutdown scheduling optimization model for the production units.

[0102] In this embodiment, the load baseline value, material flow baseline value, and material inventory baseline value of each production unit are obtained through the above steps. When constructing the production unit planned shutdown scheduling optimization model, a binary variable ∈ unit shutdown status is also introduced. u The daily change in inventory is included in the state binary variable p. t To impose constraints.

[0103] Wherein, if the binary variable of the device shutdown state ∈ u A value of "1" indicates that production unit u is running, while a value of "0" indicates that production unit u is in a planned shutdown state. Therefore, the shutdown state of the unit can be determined by the binary variable ∈ u This can maintain the continuity and stability of the entire production process throughout the scheduling cycle, reduce the impact of planned shutdowns on the entire process, and reduce the phenomenon of other units being forced to shut down due to material imbalance caused by planned shutdowns of production units. A shutdown penalty term is added to the objective function of the planned shutdown scheduling optimization model. That is, when setting constraints on unit processing capacity, a binary 0-1 variable ∈ [missing information] of the unit's operating state needs to be added to the original basic scheduling model under the constraints of unit processing capacity. u :

[0104] That is, when production unit u stops operating on day i, the load Cap of the production unit is... u,i It is 0.

[0105] In this embodiment, when a production unit shuts down, the inventory of its incoming and outgoing materials needs to change accordingly. For example, the incoming materials of the shut-down unit need to be consumed in advance before shutdown to reduce inventory and prevent inventory from reaching its limit during shutdown. Conversely, the outgoing materials of the shut-down unit need to be stockpiled before shutdown to meet the consumption needs of downstream units during shutdown and prevent downstream units from shutting down due to insufficient incoming materials. Therefore, when calculating the objective function that minimizes the daily inventory change, this buffering inventory change should be excluded and should not be included in the objective function. Therefore, the daily change is introduced into the state binary variable p. t During model solving, values ​​are assigned according to predefined rules. For example, when a certain unit is shut down, the inventory p of related materials such as feed and discharge for that unit is adjusted. t A variable with a value of "0" is not included in the objective function. A value of "1" indicates that the daily change in inventory t is included in the objective function.

[0106] Step S5: Solve the planned shutdown scheduling optimization model of the production unit to generate a planned shutdown scheduling scheme, thereby completing the optimization of the production unit scheduling scheme under the planned shutdown state.

[0107] In this embodiment, when solving the production unit planned shutdown scheduling optimization model, multiple optimization objectives are set, including: minimizing the number of unit load adjustments, minimizing the change in inventory at the beginning and end of the period, minimizing the daily change in inventory, and minimizing the unit shutdown duration. Specifically, when solving the production unit planned shutdown scheduling optimization model, corresponding weight coefficients are set according to the importance of each optimization objective. Multiple objectives are transformed into a single objective for optimization through a weighted coefficient method, thereby obtaining the final solution of the production unit planned shutdown scheduling optimization model. The objective function is as follows:

[0108] Where Obj represents the objective value of the planned shutdown scheduling optimization model for production units, and Cap... u,i Cap u,i Cap represents the load of production unit u on day i. u,0 Cap u,i N represents the initial load value of production unit u. u Indicates the number of load switching operations of the device, ∈ u Inv represents the binary variable value indicating the shutdown status of the device. t,i Int represents the liquid level of inventory t on day i. t,0 This represents the initial liquid level of the inventory on day i-1. Let μ1 represent the maximum liquid level of inventory t on day i, μ2 represent a shutdown penalty coefficient, and λ1, μ1, λ2, and λ3 represent the weighting coefficients of the corresponding parameters.

[0109] Specifically, in the actual solution process, due to differences in material flow direction and coupling degree between various devices, the resulting scheduling scheme and its effect will differ even if the coefficients of each part of the objective function are set the same when a certain production device is shut down. Therefore, when the result does not match the expectation, it is necessary to adjust the corresponding coefficients appropriately based on the solution results to achieve the desired effect. For example, if the scheduling result shows too many times the production device load is adjusted, the weight μ1 of the corresponding shutdown number term in the objective function can be increased; if the daily inventory fluctuation is large, the weight λ2 of the daily inventory change term in the objective function can be appropriately increased.

[0110] In one implementation, a 15-day basic scheduling model was constructed and solved using the method described above. In the basic scheduling model, all units are operating normally, inventory is not activated, and no product demand constraints are set. The weight coefficients in the objective function of the basic scheduling model are set as follows: λ1 = 1, λ2 = 0.001, λ3 = 5, μ1 = 500. Load setpoints and constraints for each unit, as well as initial inventory levels and upper and lower limits, are also set. The basic scheduling model is then solved to obtain the solution results. Based on these results, a planned shutdown scheduling optimization model for production units is constructed, and tests are conducted for different unit shutdown scenarios.

[0111] The following three shutdown case studies further illustrate this embodiment. The scheduling department aims to minimize load adjustments for secondary production units other than those currently shut down, while ensuring that secondary shutdowns do not lead to secondary shutdowns, and simultaneously considering inventory stability and recovery. Based on these requirements, the weighting coefficients for each item in the objective function are set as follows: λ1 = 1, λ2 = 0.001, λ3 = 1, μ1 = 500, μ2 = 1000.

[0112] Shutdown Case 1: Shutdown of Unit 1 Catalytic Cracking

[0113] In this implementation case, the No. 1 catalytic cracking unit was scheduled to shut down for 5 days, from day 6 to day 10 of the scheduling period. During this planned shutdown, no secondary shutdowns occurred in the upstream or downstream units, and a refined load allocation and inventory management strategy was implemented. During the FCC1 shutdown, the loads of both catalytic cracking units were moderately increased, effectively compensating for the capacity shortfall in the shut-down unit. Simultaneously, timely inventory accumulation ensured a continuous supply of materials. Although the loads of upstream and downstream units decreased during the shutdown, the load of the atmospheric and vacuum distillation unit remained stable throughout the scheduling cycle, and the number of load adjustments for secondary units was kept to a low level, demonstrating the foresight and flexibility of the scheduling model. Furthermore, after the shutdown, thanks to efficient inventory management and a rapid response mechanism, inventory levels quickly returned to normal operating levels, ensuring smooth subsequent production.

[0114] Case 2: Shutdown of Delayed Coking Unit

[0115] In this implementation case, the delayed coking unit was scheduled to shut down for 5 days, from day 6 to day 10 of the scheduling period. During this planned shutdown, logistical coordination and load adjustments successfully prevented a chain reaction of shutdowns to upstream and downstream units. In the preparation phase before the shutdown, the unit's load was increased in advance to maximize output and reserve sufficient resources for material needs during the shutdown. Specifically, coking wax oil was stored in backup tanks in advance as downstream units faced shutdowns, ensuring that the normal operation of upstream units would not be hindered by raw material shortages during the shutdown. Simultaneously, as the delayed coking unit's load increased, residual oil, as a feedstock for delayed coking, was consumed in large quantities for production before the shutdown, leading to a decrease in its inventory and reducing unnecessary inventory buildup during the shutdown. During the shutdown phase, residual oil consumption ceased, and inventory increased, simultaneously preparing raw materials for restarting after the shutdown. During the shutdown, effective inventory management and scheduling ensured a smooth transition between upstream and downstream units, avoiding production interruptions or secondary shutdowns caused by the delayed coking unit's shutdown. After the shutdown ended, inventory gradually recovered, ensuring that the delayed coking unit could be restarted smoothly and quickly return to normal production.

[0116] Case 3: Shutdown of the high-pressure hydrocracking unit

[0117] In this implementation case, the high-pressure hydrocracking unit was scheduled to shut down for 5 days, from days 6 to 10 of the scheduling period. Before and after the shutdown, the unit operated at maximum capacity to process as much feedstock as possible before the shutdown, minimizing feedstock backlog. This strategy also helped to stockpile necessary intermediate products, supporting the continuous operation of downstream units. Regarding inventory management, naphtha and refined oil from light hydrocarbon recovery were increased in stock before the shutdown to meet demand during the shutdown period. These were then gradually depleted during the shutdown until they approached initial levels, ensuring a rapid resumption of normal production after the shutdown. Reformate inventory was similarly depleted during the shutdown and then gradually replenished after resumption, reflecting the impact of the shutdown on the reformate unit's feedstock supply and the corresponding recovery measures. Wax oil inventory showed a similar trend: decreasing before the shutdown, increasing during the shutdown, and gradually recovering afterward, ensuring a continuous supply of wax oil as a crucial feedstock.

[0118] Therefore, when facing planned shutdowns of different units such as delayed coking and high-pressure hydrocracking, precise load allocation and inventory management—such as dynamically adjusting unit loads, pre-stocking raw materials, and coordinating the operation of upstream and downstream units—can minimize the impact of shutdowns for maintenance and troubleshooting on the overall production process. By increasing production in advance and stockpiling raw materials, upstream production remained stable during shutdowns, achieving a balance between supply and demand for materials and mitigating the risk of cascading shutdowns. Simultaneously, the scheduling and control of key intermediate material inventories ensured appropriate levels of inventory before and after shutdowns, avoiding overstocking or shortages, and enabling rapid resumption of production after shutdowns.

[0119] This specification also provides a computer-readable medium storing computer program code that, when executed by a processor, implements the attribute-based optimization method for planned shutdown scheduling of refining and chemical production units as described above.

[0120] This specification also provides an attribute-based method for optimizing the planned shutdown scheduling scheme of refining and chemical production units, including a processor-executable instruction memory and a processor for executing the instructions in the instruction memory to implement the attribute-based method for optimizing the planned shutdown scheduling scheme of refining and chemical production units as described above.

[0121] The prior description of this disclosure is provided to enable any person skilled in the art to make or use this disclosure. Various modifications to this disclosure will be apparent to those skilled in the art, and the general principles defined herein may be applied to other variations without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not intended to be limited to the examples and designs described herein, but should be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0122] Those skilled in the art will further appreciate that the various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments disclosed herein can be implemented as electronic hardware, computer software, or a combination of both. To clearly illustrate this interchangeability between hardware and software, the various illustrative components, blocks, modules, circuits, and steps are described above in a generalized manner in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. Those skilled in the art may implement the described functionality in different ways for each specific application, but such implementation decisions should not be construed as departing from the scope of the invention.

[0123] The various illustrative logic blocks, modules, and circuits described in conjunction with the embodiments disclosed herein can be implemented or performed using 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. The general-purpose processor may be a microprocessor, but in alternatives, it 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.

[0124] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of both. The software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor such that the processor can read and write information to / from the storage medium. In an alternative, the storage medium may be integrated into the processor. The processor and storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In an alternative, the processor and storage medium may reside as discrete components in the user terminal.

[0125] 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 functionality may be stored or transmitted as one or more instructions or code on or through a computer-readable medium. A computer-readable medium includes both computer storage media and communication media, encompassing any medium that facilitates the transfer of a computer program from one location to another. A storage medium may be any available medium accessible to a computer. By way of example and not limitation, such a computer-readable medium may include RAM, ROM, EEPROM, CD-ROM or other optical disc storage, 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 is accessible to a computer. Any connection is also legitimately referred to as a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (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 a medium. As used in this article, disk and disc include compact discs (CDs), laser discs, optical discs, digital multi-purpose discs (DVDs), floppy disks, and Blu-ray discs. Disks typically reproduce data magnetically, while discs reproduce data optically using lasers. Combinations of these should also be included within the scope of computer-readable media.

Claims

1. A method for optimizing a scheduling scheme for planned shutdown of a refinery production plant, characterized in that, Includes the following steps: Step S1: Collect information on production equipment and material inventory based on the actual production process of the refinery; Step S2: Construct a basic scheduling model for the refinery under normal operating conditions based on the collected production unit information and material inventory information; Step S3: Solve the basic scheduling model based on the processing device information and material inventory information to obtain the load baseline value, material flow baseline value and material inventory baseline value of each production device; Step S4: Based on the load baseline value, material flow baseline value, and material inventory baseline value of each production unit obtained by solving, and in conjunction with the refinery's planned shutdown arrangements, construct a planned shutdown scheduling optimization model for the production units. Step S5: Solve the planned shutdown scheduling optimization model of the production unit to generate a planned shutdown scheduling scheme, thereby completing the optimization of the production unit scheduling scheme under the planned shutdown state.

2. The method for optimizing a scheduling scheme for planned shutdowns of a refinery production plant according to claim 1, characterized in that, The production unit information includes information on atmospheric and vacuum distillation units and secondary processing units involved in production. The material inventory information includes raw material information, intermediate material information, and product information. The planned shutdown scheduling scheme for the refining and chemical production units is constructed based on the collected production unit information and material inventory information to build a basic scheduling model simulating the normal operation of the refinery.

3. The method according to claim 2, wherein the method is characterized by, The optimization method for planned shutdown scheduling of refining and chemical production units, after obtaining information on production units and material inventory, establishes constraints for a basic scheduling model based on the obtained production unit information. The constraints for the basic scheduling model include: unit processing capacity constraints, unit material balance constraints, inventory inflow and outflow balance constraints, inventory upper and lower limits constraints, raw material supply constraints, and unit feed ratio constraints.

4. The method for optimizing a scheduling scheme for planned shutdowns of a refining and petrochemical production facility according to claim 3, wherein, The device processing capacity constraints are shown below: Where U represents the set of production units, Cap u,i represents the plant load of the production plant u on day i, This indicates the upper limit of the load of production unit u. This indicates the lower limit of the load of production unit u.

5. The method for optimizing a scheduling scheme for planned shutdowns of a refinery production plant according to claim 3, wherein, The method for optimizing the scheduling scheme for planned shutdowns of refining and chemical production units according to claim 4 is characterized in that, when constructing material balance constraints, the method constructs a material balance model based on the material receipt and payment and the material flow relationship between processing units; wherein, the material balance model includes material flow analysis and material supply and demand matching strategy; when the basic scheduling model fails to solve, the method for optimizing the scheduling scheme for planned shutdowns of refining and chemical production units applies unit material balance constraints to the basic scheduling model based on the material balance model, thereby adjusting the initial parameters of the material balance model to obtain a feasible solution while ensuring that the material inventory remains unchanged during the scheduling cycle.

6. The method for optimizing a scheduling scheme for planned shutdowns of a refinery production plant according to claim 5, wherein, Material balance constraints include secondary unit material balance constraints and diversion unit material balance constraints. The optimization method for the planned shutdown scheduling scheme of refining and chemical production units uses a material balance model to apply secondary unit material balance constraints and diversion unit material balance constraints to the basic scheduling model, thereby adjusting the initial parameters of the material balance model while keeping the material inventory unchanged during the scheduling cycle.

7. The method for optimizing a scheduling scheme for planned shutdowns of a refinery production plant according to claim 6, characterized in that, The secondary unit material balance constraint conditions are shown below: Where U represents the set of production units, Cap u,i represents the production load of the production device u on the i-th day, yield u,s represents the yield of each offtake stream s of production unit u, Mass s,i represents the flow rate of each stream s of the production unit u on day i.

8. The method for optimizing a scheduling scheme for planned shutdowns of a refinery production plant according to claim 6, characterized in that, The split device material balance constraint is shown below: Where U represents the set of production units, U SPL representing a set of shunt devices, This indicates the feed collection of the diversion device. This indicates the collection of materials discharged from the diversion device. Mass s,i Mass flow rate of each stream s on day i.

9. The method of claim 3, wherein the method further comprises: determining a plurality of scheduling schemes for the planned shutdown of the refinery production facility; and selecting one of the plurality of scheduling schemes based on the optimization of the plurality of scheduling schemes. The inventory in-out balance constraint condition is shown as follows: where Inv t,i represents the inventory level of inventory t on day i, The collection of incoming materials from inventory. This represents the set of materials shipped from inventory. Inv t,i represents the liquid level of the inventory t on the i-th day, Inv t,i-1 represents the inventory t level on day i-1.

10. The method of claim 3, wherein the method further comprises: determining a plurality of scheduling schemes for the planned shutdown of the refinery production facility; and selecting one of the plurality of scheduling schemes based on the optimization criteria. The upper and lower inventory constraints are shown below: wherein, This represents the upper limit of the can storage capacity of inventory t. This represents the lower limit of the tank storage capacity for inventory t.

11. The method of claim 3, wherein the method further comprises: determining a plurality of scheduling schemes for the planned shutdown of the refinery production facility; and selecting one of the plurality of scheduling schemes based on the optimization of the plurality of scheduling schemes. The scheduling scheme optimization method for coping with the planned shutdown of the refining and chemical production device determines the constraint condition of a basic scheduling model, solves the basic scheduling model with the minimum load fluctuation of each production device as an optimization objective and the unchanged material inventory as a special constraint, and thus obtains the load reference value, the material flow reference value and the material inventory reference value of each production device; wherein the optimization objective is as follows: Where Obj represents the optimization objective value of the basic scheduling model. Cap u,i represents the production load of the production device u on the i-th day, Cap u,0 a set value indicating the load of the display device, λ1 represents the weighting coefficient for the load deviation of the equipment. μ1 represents the device load switching penalty coefficient. N u indicates the number of times the device load is switched.

12. The method of claim 1, wherein the method is characterized by: The scheduling scheme optimization method for the planned shutdown of the refining and chemical production device introduces a device shutdown state binary variable ∈ u and the daily inventory change into the state binary variable p t to perform constraints; wherein, If the binary variable of the plant shutdown state ∈ u is "1", it means that the production plant u is running, and if it is "0", it means that the production plant u is in a planned shutdown state. If the daily inventory change amount is counted in the state binary variable p t "1" indicates that the daily inventory change amount is counted, and "0" indicates that the daily inventory change amount is not counted.

13. The method for optimizing the planned shutdown scheduling of refining and chemical production units according to claim 1, characterized in that, The planned shutdown scheduling optimization model for production units has multiple optimization objectives, including: minimizing the number of unit load adjustments, minimizing the initial and final inventory changes, minimizing daily inventory changes, and minimizing unit shutdown duration. The optimization method for the planned shutdown scheduling scheme of refining and chemical production units, when solving the planned shutdown scheduling optimization model, sets corresponding weight coefficients based on the importance of each optimization objective. Multiple objectives are transformed into a single objective through coefficient weighting for optimization, thereby obtaining the final solution of the planned shutdown scheduling optimization model for production units.

14. The method of claim 1, wherein the method further comprises: determining a plurality of scheduling scenarios for the planned shutdown of the refinery production facility; and determining a plurality of scheduling scenarios for the planned shutdown of the refinery production facility based on the plurality of scheduling scenarios. 19 The optimization objective of the production device planned shutdown scheduling optimization model is shown as follows: Where Obj represents the optimization objective value of the planned shutdown scheduling optimization model for production units. Cap u,i represents Cap u,i represents the load of the production unit u on day i, Cap u,0 Cap u,i This represents the initial load value of production unit u. N u indicates the number of times the display device load is switched, ∈ u represents the binary variable value of the device down state, Inv t,i represents the liquid level of the inventory t on day i, Int t,0 represents the initial value of the inventory level on day i-1, Let represent the maximum liquid level of inventory t on day i. μ2 represents a shutdown penalty coefficient. λ1, μ1, λ2, and λ3 are the weighting coefficients of the corresponding parameters.

15. A computer readable medium having stored computer program code, the computer program code comprising instructions for causing a computer to perform the method of any one of claims 1 to 14. 15 The computer program code, when executed by a processor, implements the method as described in any one of claims 1-14.

16. A method and apparatus for optimizing the planned shutdown scheduling of refining and chemical production units, characterized in that, include: Memory is used to store instructions that can be executed by the processor; as well as A processor for executing the instructions to implement the method as described in any one of claims 1-14.

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