Method for optimizing planned shutdown scheduling scheme for refining production device

Abstract

The invention discloses a planned shutdown scheduling scheme optimization method for a refinery production device, and the method comprises the steps: collecting production device information and material inventory information according to the actual production process of a refinery plant, and constructing a basic scheduling model in a conventional operation state of the refinery plant based on the collected production device information and material inventory information. Solving the basic scheduling model according to the processing device information and the material inventory information to obtain a load reference value, a material trend reference value and a material inventory reference value of each production device; and then, on the basis of the solved load reference value, material trend reference value and material inventory reference value of each production device, a production device planned shutdown scheduling optimization model is constructed in combination with the refinery planned shutdown arrangement. And finally, solving the planned shutdown scheduling optimization model of the production device, and generating a planned shutdown scheduling scheme, thereby completing optimization of the scheduling scheme of the production device in the planned shutdown state.

Description

Technical Field

[0001] The present invention relates to the field of refinery production scheduling, and in particular to a method for optimizing a planned shutdown scheduling scheme for a refining production unit. Background Art

[0002] The oil refining industry is one of the important pillar industries of the national economy, and is responsible for converting crude oil into various petroleum products. As the core facility of the oil refining industry, the efficient and safe operation of the production process of the oil refinery plays an irreplaceable role in ensuring energy supply and promoting economic development. The production process of an oil refinery usually includes two major links: primary processing and secondary processing. Secondary processing refers to the deep processing of the intermediate products produced by primary processing to obtain higher quality petroleum products. These secondary devices cover a series of complex chemical reaction processes such as catalytic cracking, hydrocracking, delayed coking, and reforming. They are not only highly adaptable to raw materials, but also can produce 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 equipment in refineries is not static. Due to factors such as market demand fluctuations, changes in raw material quality, and equipment maintenance needs, refineries need to regularly shut down secondary equipment for maintenance to ensure long-term stable operation of the equipment and maximize production efficiency. Planned shutdowns for maintenance and troubleshooting are a systematic project that involves orderly shutdown of equipment, material balance, inventory management, and other aspects, which have a significant impact on the overall operating plan and economic benefits of the refinery. Traditional shutdown scheduling often relies on experienced operators to make manual decisions. Although this method is intuitive, it is often difficult to balance efficiency, cost, and safety when dealing with complex and changing production situations, and it is easy to lead to waste of resources, environmental pollution, and safety hazards.

[0004] In recent years, with the continuous improvement of industrial automation and informatization, the production management of refineries is gradually developing in the direction of intelligence and refinement. Advanced modeling and optimization technologies, such as linear programming, nonlinear programming, mixed integer programming, intelligent algorithms, etc., provide new solutions for refinery shutdown scheduling. By constructing a mathematical model, 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 strategy, and equipment maintenance plan, the revision efficiency and implementation effect of the full-process production scheduling plan caused by planned shutdown and maintenance and defect elimination of the device can be significantly improved, unnecessary shutdown time and cost can be reduced, and the comprehensive competitiveness of the refinery can be improved. Therefore, the development of a more accurate, efficient, and intelligent shutdown scheduling model and optimization algorithm has become a key issue that needs to be urgently solved in the refining industry. Summary of the invention

[0005] A brief summary of one or more aspects is given below to provide a basic understanding of these aspects. This summary is not an exhaustive overview of all conceived aspects, and is neither intended to identify the key or critical elements of all aspects nor to define the scope of any or all aspects. Its only purpose is to give some concepts of one or more aspects in a simplified form as a prelude to a more detailed description that will be given later.

[0006] The purpose of the present invention is to solve the above-mentioned problems and to provide a method and method for optimizing the planned shutdown scheduling scheme for refining and chemical production units, so as to solve the problem that the unit scheduling in the shutdown event of refining and chemical enterprises relies on manual experience, has low efficiency and poor effect, in order to provide a strong scientific basis and technical support for the planned shutdown scheduling of refineries.

[0007] The technical solution of the present invention is:

[0008] The present invention provides a method for optimizing a planned shutdown scheduling scheme for a refining production device, comprising the following steps:

[0009] Step S1: Collect production equipment information and material inventory information according to the actual production process of the refinery;

[0010] Step S2: constructing a basic scheduling model under the normal operation state of the refinery based on the collected production unit information and material inventory information;

[0011] Step S3: Solve the basic scheduling model according to the processing device information and the material inventory information to obtain the load reference value, material flow reference value and material inventory reference value of each production device;

[0012] Step S4: Based on the load benchmark value, material trend benchmark value and material inventory benchmark value of each production unit obtained by solving, combined with the planned shutdown arrangement of the refinery, a planned shutdown scheduling optimization model for the production unit is constructed;

[0013] Step S5: Solve the production unit planned shutdown scheduling optimization model to generate a planned shutdown scheduling plan, thereby completing the optimization of the production unit scheduling plan under the planned shutdown state.

[0014] According to an embodiment of the attribute-based optimization method for the planned shutdown scheduling scheme for refining and chemical production units of the present invention, the production unit information includes the information of the atmospheric and vacuum units and the secondary processing units involved in the production, and the material inventory information includes the raw material information, the intermediate material information, and the product information. The planned shutdown scheduling scheme for refining and chemical production units constructs a basic scheduling model that simulates the normal operation state of the refinery based on the collected production unit information and material inventory information.

[0015] According to an embodiment of the attribute-based optimization method for the planned shutdown scheduling scheme for refining and chemical production units of the present invention, after obtaining the production unit information and material inventory information, the optimization method for the planned shutdown scheduling scheme for refining and chemical production units establishes the constraints of the basic scheduling model based on the obtained production unit information; wherein the constraints of the basic scheduling model include: unit processing capacity constraints, unit material balance constraints, inventory in-and-out balance constraints, inventory upper and lower limit constraints, raw material supply constraints, and unit feed ratio constraints.

[0016] According to an embodiment of the attribute-based optimization method for the planned shutdown scheduling scheme of a refining production unit of the present invention, the processing capacity constraint conditions of the unit are as follows:

[0017]

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

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

[0020] represents the upper limit of the device load of production device u,

[0021] Indicates the lower limit of the device load of production device u.

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

[0023] According to an embodiment of the attribute-based optimization method for the planned shutdown scheduling scheme for a refining and chemical production unit of the present invention, the material balance constraints include secondary device material balance constraints and diversion device material balance constraints. The method for optimizing the planned shutdown scheduling scheme for a refining and chemical production unit uses a material balance model to perform secondary device material balance constraints and diversion device material balance constraints on a 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 the planned shutdown scheduling scheme of a refining production unit of the present invention, the material balance constraints of the secondary unit are as follows:

[0025]

[0026] Where U represents the set of production devices,

[0027] Cap u,i represents the production load of production unit u on day i,

[0028] yield u,s represents the yield of each output stream s of production unit u,

[0029] Mass s,i Represents the flow rate of each stream s of production unit u on the i-th day.

[0030] According to an embodiment of the attribute-based optimization method for the planned shutdown scheduling scheme of a refinery production unit of the present invention, the material balance constraint conditions of the diversion unit are as follows:

[0031]

[0032] Where U represents the set of production devices,

[0033] U SPL Represents a collection of diversion devices,

[0034] Represents the feed collection of the splitter,

[0035] Indicates the discharging collection of the diversion device,

[0036] Mass s,i Represents the flow rate of each stream s in the diversion device on the i-th day.

[0037] According to an embodiment of the attribute-based optimization method for the planned shutdown scheduling scheme of a refining production unit of the present invention, the inventory in-and-out balance constraint conditions are as follows:

[0038]

[0039] Among them, Inv t,i represents the inventory of inventory t on day i,

[0040] A collection of stock feeds,

[0041] Represents the collection of inventory outflows,

[0042] Inv t,i represents the liquid level of inventory t on day i,

[0043] Inv t,i-1 represents the inventory level t on day i-1.

[0044] According to an embodiment of the attribute-based optimization method for the planned shutdown scheduling scheme of a refining production unit of the present invention, the upper and lower limit constraints of inventory are as follows:

[0045]

[0046] in, represents the upper limit of the tank capacity of inventory t,

[0047] Represents the lower limit of the tank storage capacity of inventory t.

[0048] According to an embodiment of the attribute-based optimization method for the planned shutdown scheduling scheme for a petrochemical production unit of the present invention, after determining the constraint conditions of the basic scheduling model, the optimization method for the planned shutdown scheduling scheme for a petrochemical production unit takes the minimum load fluctuation of each production unit as the optimization goal and the unchanged material inventory as the special constraint, and solves the basic scheduling model to obtain the load reference value, material trend reference value and material inventory reference value of each production unit; wherein the optimization goal is as follows:

[0049]

[0050] Among them, Obj represents the optimization target value of the basic scheduling model,

[0051] Cap u,i represents the production load of production unit u on day i,

[0052] Cap u,0 Indicates the set value of the device load,

[0053] λ 1 Indicates the device load deviation weight coefficient,

[0054] μ 1 Indicates the device load switching penalty coefficient.

[0055] N u Indicates the number of load switching times of the device.

[0056] According to an embodiment of the attribute-based optimization method for a planned shutdown scheduling scheme for a petrochemical production unit of the present invention, the method for optimizing a planned shutdown scheduling scheme for a petrochemical production unit introduces a device shutdown state binary variable ∈ 1 when constructing a planned shutdown scheduling optimization model for a production unit. u and daily changes in inventory are included in the state binary variable p t To constrain; among them,

[0057] If the device shutdown state binary variable ∈ u If the value is "1", it means that the production unit u is running, and if it is "0", it means that the production unit u is in a planned shutdown state;

[0058] If the daily change in inventory is included in the state binary variable p t If the value is "1", it means that the daily change of inventory t is included. If it is "0", it means that the daily change of inventory t is not included.

[0059] According to an embodiment of the attribute-based optimization method for the planned shutdown scheduling scheme for petrochemical production units of the present invention, the planned shutdown scheduling optimization model for production units is provided with multiple optimization objectives, including: the optimization objectives include minimizing the number of unit load adjustments, minimizing the change between the beginning and the end of the inventory period, minimizing the daily change in inventory, and minimizing the unit shutdown duration; wherein, when solving the planned shutdown scheduling optimization model for petrochemical production units, the optimization method for the planned shutdown scheduling scheme for petrochemical production units sets corresponding weight coefficients according to the importance of each optimization objective, and converts multiple objectives into a single objective in a coefficient weighted form for optimization, thereby obtaining the final solution of the planned shutdown scheduling optimization model for production units.

[0060] According to an embodiment of the attribute-based optimization method for the planned shutdown scheduling scheme of a refining production unit of the present invention, the optimization objectives of the planned shutdown scheduling optimization model of the production unit are as follows:

[0061]

[0062] Wherein, Obj represents the optimization target value of the planned shutdown scheduling optimization model of the production unit,

[0063] Cap u,i Cap u,i represents the load of production unit u on day i,

[0064] Cap u,0 Cap u,i represents the initial value of the production unit u load,

[0065] N u Indicates the number of load switching times of the device.

[0066] ∈ u A binary variable value representing the shutdown status of the device,

[0067] Inv t,i represents the liquid level of inventory t on day i,

[0068] Int t,0 represents the initial value of the inventory level on day i-1,

[0069] represents the maximum liquid level of inventory t on day i,

[0070] μ 2 represents a shutdown penalty coefficient,

[0071] λ 1 , μ 1 , 2 , 3 are the weight coefficients of the corresponding parameters respectively.

[0072] The present invention also provides a computer readable medium storing a computer program code, wherein the computer program code implements the method as described above when executed by a processor.

[0073] The present invention also provides a method and device for optimizing a planned shutdown scheduling scheme for a refining production unit, comprising:

[0074] a memory for storing instructions executable by a processor; and

[0075] A processor is used to execute the instructions to implement the method as described above.

[0076] Compared with the prior art, the present invention has the following beneficial effects: the present invention aims at the planned shutdown of the refining production unit, takes the scheduling scheme solved by the basic scheduling model under the normal operation state of the refinery as the benchmark, and constructs an intelligent production unit planned shutdown scheduling optimization model to automatically optimize the scheduling scheme of the production unit under the planned shutdown state. Through the present invention, the limitation of relying on manual experience under the traditional scheduling method is broken through, and the efficiency and accuracy of scheduling decisions are significantly improved, so that the refinery can quickly respond to market changes and production needs, and automatically identify the optimal shutdown and restart strategy, greatly shorten the unplanned shutdown time, and reduce the work intensity. At the same time, through the present invention, refined management of inventory, material balance and device load is achieved, and the additional costs caused by shutdown, such as waste of raw materials and increased energy consumption, are effectively reduced. Through preventive maintenance and advance planning, the risk of equipment failure rate and secondary shutdown is reduced, and production safety and stability are guaranteed. In addition, the present invention also takes into account the actual operating conditions, aiming to reduce the number of device switching and avoid energy waste and equipment wear caused by frequent start and stop. By optimizing the load adjustment of the equipment and the flow of materials, a smooth transition of the production process is ensured, the difficulty of operation is reduced, and the overall stability of the production system and the work efficiency of operators are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] The above features and advantages of the present invention can be better understood after reading the detailed description of the embodiments of the present disclosure in conjunction with the following drawings. In the drawings, the components are not necessarily drawn to scale, and components with similar related properties or features may have the same or similar reference numerals.

[0078] Figure 1 The flowchart shows an embodiment of the attribute-based optimization method for the planned shutdown scheduling scheme of a refinery production unit according to the present invention. DETAILED DESCRIPTION

[0079] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of the present application. For ordinary technicians in this field, the present application can also be applied to other similar scenarios based on these drawings without creative work. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.

[0080] As shown in this application and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0081] Unless otherwise specifically stated, the relative arrangement, numerical expressions and numerical values ​​of the parts and steps set forth in these embodiments do not limit the scope of the present application. At the same time, it should be understood that, for ease of description, the sizes of the various parts shown in the accompanying drawings are not drawn according to the actual proportional relationship. The technology, method and equipment known to those of ordinary skill in the relevant field may not be discussed in detail, but in appropriate cases, the technology, method and equipment should be considered as a part of the authorization specification. In all examples shown and discussed here, any specific value should be interpreted as being merely exemplary, rather than as a limitation. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters represent similar items in the following drawings, so that once a certain item is defined in an accompanying drawing, it does not need to be further discussed in subsequent drawings.

[0082] When describing the embodiments of the present invention in detail, for the sake of convenience, the cross-sectional views showing the device structure will not be partially enlarged according to the general scale, and the schematic views are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0083] In the description of the present application, it should be understood that the directions or positional relationships indicated by directional words such as "front, back, up, down, left, right", "lateral, vertical, perpendicular, horizontal" and "top, bottom" are usually based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present application and simplifying the description. Unless otherwise specified, these directional words do not indicate or imply that the device or element referred to must have a specific direction or be constructed and operated in a specific direction, and therefore cannot be understood as limiting the scope of protection of the present application; the directional words "inside and outside" refer to the inside and outside relative to the contours of each component itself.

[0084] Disclosed herein is an embodiment of an attribute-based optimization method for a planned shutdown scheduling scheme for a refining production unit. Figure 1 This is a flow chart showing an embodiment of the method for optimizing the scheduled shutdown scheduling scheme for a refinery production unit based on attributes of the present invention. Figure 1 , the following is a detailed description of each step of the attribute-based optimization method for the planned shutdown scheduling scheme for a refining production unit.

[0085] Step S1: Collect production equipment information and material inventory information according to the actual production process of the refinery.

[0086] Step S2: Construct a basic scheduling model under the normal operation state of the refinery based on the collected production unit information and material inventory information.

[0087] In this embodiment, the production unit information includes the atmospheric and vacuum unit information and secondary processing unit information involved in the production, specifically the atmospheric and vacuum unit, secondary processing unit, and detailed information such as raw materials, intermediate materials and products. Among them, 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 according to the actual production process of the refinery, a basic scheduling model under the normal operation state of the simulated refinery is constructed according to the collected production unit information and material inventory information.

[0088] Specifically, in this embodiment, the production unit has 2 or more sets of atmospheric and vacuum devices and multiple sets of secondary processing devices, including but not limited to catalytic cracking, delayed coking, residual oil hydrogenation, hydrocracking, reforming pre-hydrogenation, continuous reforming, gasoline and diesel hydrogenation, S-zorb, oil slurry topping, light hydrocarbon recovery, solvent deasphalting, alkylation, aromatics combined unit, 1# gas fraction, 2# gas fraction and other devices. The inventory takes into account the inventory of raw materials, products and some intermediate materials, including but not limited to aviation fuel, hydrogenation material, light dirty oil, wax oil, hydrogenation tail oil, residual heavy oil, residual oil, reforming material, oil slurry, gas fraction material, catalytic gasoline, commercial gasoline, commercial diesel, aviation fuel products, paraxylene products, liquefied gas products, asphalt products, deoiled asphalt, coking wax oil, light hydrocarbon recovery naphtha, refined oil, reforming oil, purchased xylene and other material inventories. Based on these production unit information and material inventory information, a basic scheduling model simulating the normal operation state of the refinery can be constructed.

[0089] Step S3: Solve the basic scheduling model according to the processing device information and the material inventory information to obtain the load reference value, material flow reference value and material inventory reference value of each production device.

[0090] In this embodiment, before solving the basic scheduling model, it is also necessary to establish the constraints of the basic scheduling model based on the acquired production device information. The constraints of the basic scheduling model include: device processing capacity constraints, device material balance constraints, inventory in-out balance constraints, inventory upper and lower limit constraints, raw material supply constraints, and device feed ratio constraints. When solving the basic scheduling model, these constraints are set to constrain the scheduling plan obtained by solving the basic scheduling model to conform to the actual production process.

[0091] Specifically, in this embodiment, when setting the device processing capacity constraint conditions, considering the need to constrain the production device load, the following constraint conditions are set:

[0092]

[0093] Among them, U represents the set of production equipment, Cap u,i represents the load of production unit u on day i, represents the upper limit of the device load of production device u, Indicates the lower limit of the device load of production device u. In the actual production process, considering the actual production situation, the upper limit of the production device operating load can be set to 110% of the design load, and the lower limit of the production device operating load can be set to 70%. The upper and lower limits of inventory are set according to the capacity limit of the actual storage tank, and the initial inventory of each material can be set.

[0094] In this embodiment, when constructing material balance constraints, we must first sort out the material receipt and payment and material flow relationships between processing devices according to the actual production process to ensure that each link in the entire production process is closely connected through material flow to form a coherent material balance model. Among them, the material balance model includes material flow analysis and material supply and demand matching strategy. When the basic scheduling model fails to be solved, the basic scheduling model can be constrained by the device material balance based on the material balance model, so as to ensure that the material inventory remains unchanged during the scheduling cycle and adjust the initial parameters of the material balance model to solve and obtain a feasible solution.

[0095] Specifically, in this embodiment, the material balance constraints include the secondary device material balance constraints and the diversion device material balance constraints. When the basic model cannot obtain a feasible solution, it is necessary to further adjust the basic scheduling model for the material balance of the entire process. Under the constraints of the secondary device material balance and the diversion device material balance, the initial parameters of the basic model are adjusted by using material flow analysis and material supply and demand matching strategies, so as to ensure that the material inventory remains unchanged during the scheduling cycle and achieve a dynamic balance of material supply and demand in the entire process. If the inventory of a certain material decreases, the output of the material can be increased by increasing the load of the upstream device, or the demand for the material can be reduced by reducing the load of the downstream device, reducing product sales, etc., so as to achieve a balance between supply and demand.

[0096] Among them, the material balance constraints of the secondary device are as follows:

[0097]

[0098] Among them, U represents the set of production equipment, Cap u,i represents the production load of production unit u on day i, yield u,srepresents the yield of each output stream s of production unit u, Mass s,i Represents the flow rate of each stream s of production unit u on the i-th day.

[0099] The material balance constraints of the splitter are as follows:

[0100]

[0101] Among them, U represents the set of production devices, U SPL Represents a collection of diversion devices, Represents the feed collection of the splitter, Indicates the feed and discharge collection of the splitter, Mass s,i Represents the flow rate of each stream s in the diversion device on the i-th day.

[0102] In this embodiment, when setting the inventory in-out balance constraint conditions, it is necessary to consider the inventory in- and out-of-stock conditions, and the constraint conditions are as follows:

[0103]

[0104] Among them, Inv t,i represents the inventory of inventory t on day i, represents a collection of stock feeds, Indicates the collection of inventory outflows, Inv t,i Inv represents the liquid level of inventory t on day i, t,i-1 represents the inventory level t on day i-1.

[0105] When setting the upper and lower limit constraints of inventory, the tank storage capacity corresponding to different inventories is considered. The constraints are as follows:

[0106]

[0107] in, represents the upper limit of the tank capacity of inventory t, Indicates the lower limit of the tank storage capacity of inventory t. In one embodiment, the following production units and material inventories may be set with constraints on design process and customization, including but not limited to: the proportion of oil slurry and deoiled asphalt in the feed of the delayed coking unit does not exceed 20%, the slag blending ratio of the feed of the residue oil hydrogenation unit is 50%-55%, the proportion of slag and heavy oil in the feed of the catalytic cracking unit does not exceed 70%, the proportion of purchased xylene in the feed of the aromatics complex does not exceed 50%, the straight-run diesel can be composed of atmospheric vacuum normal line 1 and normal line 2, the straight-run wax oil can be composed of atmospheric vacuum normal line 3 and reduced line 1, reduced line 2, reduced line 3, etc.

[0108] In this embodiment, after determining the constraints of the basic scheduling model, the basic scheduling model is solved with the minimum load fluctuation of each production unit as the optimization goal and the unchanged material inventory as the special constraint, and the solved scheduling scheme is used as the benchmark of the planned shutdown scheduling optimization model of the production unit, thereby obtaining the load benchmark value, material trend benchmark value and material inventory benchmark value of each production unit. Among them, the optimization objectives of the basic scheduling model are as follows:

[0109]

[0110] Among them, Obj represents the optimization target value of the basic scheduling model, Cap u,i represents the production load of production unit u on day i, Cap u,0 Indicates the set value N of the device load u Indicates the number of load switching times of the device, λ 1 Indicates the device load deviation weight coefficient, μ 1 Indicates the device load switching penalty coefficient.

[0111] In addition, in this embodiment, when solving the basic scheduling model, some special constraints are involved, including but not limited to: all units are operating normally, no inventory is used, no product demand restrictions, product demand is considered to be unrestricted, and all products are sold according to actual production, etc. The basic scheduling model is optimized and solved in combination with these special constraints, and the solution obtained is the optimal operating point. Under normal operation, the production process of the refinery enterprise is maintained at this benchmark state, providing a basis for the subsequent production unit planned unit shutdown scheduling optimization model.

[0112] Step S4: Based on the load benchmark value, material trend benchmark value and material inventory benchmark value of each production unit obtained by solving, combined with the planned shutdown arrangement of the refinery, a planned shutdown scheduling optimization model for the production unit is constructed.

[0113] In this embodiment, the load reference value, material trend reference value and material inventory reference value of each production device are obtained through the above steps. When constructing the planned shutdown scheduling optimization model of the production device, the device shutdown state binary variable ∈ u and daily changes in inventory are included in the state binary variable p t To constrain.

[0114] Among them, if the device shutdown state binary variable ∈ u If the value is "1", it means that the production device u is running, and if it is "0", it means that the production device u is in a planned shutdown state. u, 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 that other devices are forced to shut down due to material imbalance due to planned shutdowns of production units, and add shutdown penalties to the objective function of the planned shutdown scheduling optimization model of production units. That is, when setting the device processing capacity constraints, it is also necessary to add the binary 0-1 variable ∈ of the device operating status to the device processing capacity constraints of the original basic scheduling model. u :

[0115]

[0116] That is, when production unit u stops working on day i, the load Cap of the production unit is u,i is 0.

[0117] In this embodiment, when a production unit is shut down, the inventory of the feed and discharge of the production unit needs to change accordingly. For example, the feed of the shut-down unit needs to be consumed in advance before the shutdown to reduce the inventory and prevent the inventory from reaching the upper limit during the shutdown period. The discharge of the shut-down unit needs to be stored in a tank before the shutdown to meet the consumption of the downstream unit during the shutdown period and prevent the downstream unit from shutting down due to insufficient feed. Therefore, when calculating the objective function of minimizing the daily change in inventory, this part of the inventory change that acts as a buffer should be excluded and should not be included in the objective function. Therefore, the daily change is introduced to be included in the state binary variable p t , and assign values ​​according to the established rules when solving the model. For example, when a device is shut down, the inventory of related materials such as the feed and discharge of the device is p t If the variable is assigned a value of "0", it is not included in the objective function. If the value is "1", it means that the daily change in inventory t is included in the objective function.

[0118] Step S5: Solve the production unit planned shutdown scheduling optimization model to generate a planned shutdown scheduling plan, thereby completing the optimization of the production unit scheduling plan under the planned shutdown state.

[0119] In this embodiment, when solving the optimization model for planned shutdown scheduling of production equipment, multiple optimization objectives are set, including: the optimization objectives include the minimum number of device load adjustments, the minimum change in inventory at the beginning and end of the period, the minimum daily change in inventory, and the shortest device shutdown time. Among them, when solving the optimization model for planned shutdown scheduling of production equipment, the corresponding weight coefficient is set according to the importance of each optimization objective, and multiple objectives are converted into a single objective in the form of coefficient weighting for optimization, so as to obtain the final solution of the optimization model for planned shutdown scheduling of production equipment. The objective function is as follows:

[0120]

[0121] Among them, Obj represents the optimization target value of the planned shutdown scheduling optimization model of the production unit, Cap u,i Cap u,i Cap represents the load of production unit u on day i, u,0 Cap u,i represents the initial value of the production unit u load, N u Indicates the number of load switching times of the device, ∈ u Indicates the binary variable value of the device shutdown state, Inv t,i Int represents the liquid level of inventory t on day i. t,0 represents the initial value of the inventory level on day i-1, represents the maximum liquid level of inventory t on day i, μ 2 represents a shutdown penalty coefficient, λ 1 , μ 1 , 2 , 3 are the weight coefficients of the corresponding parameters respectively.

[0122] Specifically, in the actual solution process, due to the different material flows of each device and the degree of coupling with other devices, when a certain production device is shut down, even if the coefficients of each part of the objective function are set the same, the solved scheduling plan and its effect will be different. Therefore, when the result does not meet the expectations, it is necessary to adjust the corresponding coefficients appropriately according to the solution results to achieve the desired effect. For example, if the number of production device load adjustments in the scheduling result is too many, the weight μ of the corresponding shutdown number term in the objective function can be increased. 1 ; If the daily fluctuation of inventory is large, the weight λ of the daily change of inventory in the objective function can be appropriately increased 2 .

[0123] In one embodiment, a 15-day basic scheduling model is constructed and solved by the above method. In the basic scheduling model, all devices are operating normally, inventory is not enabled, and no product demand constraints are set. Among them, the weight coefficients of each item in the objective function of the basic scheduling model are set as follows: 1 =1,λ 2 =0.001,λ 3 =5,μ 1 =500, and set the load setting value and load constraint of each device, as well as the initial liquid level and upper and lower limits of each inventory. Then solve the basic scheduling model to obtain the solution result, and use this scheduling result as a benchmark to build a production device planned shutdown scheduling optimization model on this basis, and test the shutdown conditions of different devices:

[0124] Three shutdown cases are listed below to further explain this embodiment in detail. The dispatch department expects that the load adjustment times of other secondary production devices other than the shutdown device will be reduced as much as possible, under the premise that no secondary shutdown will occur in other unplanned shutdown devices, and on this basis, inventory stability and inventory recovery will be taken into account. According to the above requirements, the weight coefficients of each item in the objective function are set as follows: λ 1 =1,λ 2 =0.001,λ 3 =1,μ 1 =500,μ 2 =1000.

[0125] Shutdown Case 1: Shutdown of No. 1 Catalytic Cracking Unit

[0126] In this implementation case, the 1# catalytic cracking unit was scheduled to shut down from the 6th to the 10th day of the scheduling period, for a total of 5 days. During the planned shutdown of the 1# catalytic cracking unit, no secondary shutdown events occurred in the upstream and downstream units, and a refined load distribution and inventory management strategy was provided. During the shutdown of FCC1, the loads of the two catalytic cracking units were moderately increased, effectively making up for the production capacity gap of the shutdown unit. At the same time, timely inventory accumulation ensured the continuous supply of materials. Although the loads of the upstream and downstream units decreased during the shutdown, the load of the atmospheric and vacuum distillation unit remained stable during the entire scheduling cycle, and the number of load adjustments of the secondary units was also controlled at a low level, reflecting the foresight and flexibility of the scheduling model. At the same time, after the shutdown, thanks to the efficient inventory management and rapid response mechanism, the inventory level quickly returned to the normal working level to ensure the smooth progress of subsequent production.

[0127] Shutdown Case 2: Delayed Coking Unit Shutdown

[0128] In this implementation case, the delayed coking unit is scheduled to shut down from the 6th to the 10th day of the scheduling period, for a total of 5 days. During the planned shutdown of the delayed coking unit, logistics coordination and load adjustment were used to successfully avoid the impact of chain shutdowns on upstream and downstream units. In the preparation stage before the shutdown of the delayed coking unit, the unit load was increased in advance to maximize output and reserve sufficient resources for material needs during the shutdown. That is, when the downstream unit is about to face shutdown, the coking wax oil is stored in the spare storage tank in advance to ensure that the normal operation of the upstream unit during the shutdown will not be hindered by the shortage of raw materials. At the same time, as the load of the delayed coking unit increases, the residual oil, as the raw material of delayed coking, is consumed in large quantities for production before the shutdown, and its inventory has shown a downward trend to reduce unnecessary inventory backlogs during the shutdown period. Entering the shutdown stage, the consumption of residual oil stops, and the inventory increases accordingly, which is also to prepare raw materials for the restart after the shutdown. During the shutdown period, through effective inventory management and scheduling arrangements, a smooth transition between upstream and downstream units is ensured, avoiding production interruptions or secondary shutdowns caused by the shutdown of the delayed coking unit. After the shutdown ended, inventories gradually recovered, ensuring that the delayed coking unit could be restarted smoothly and quickly return to normal production.

[0129] Shutdown Case 3: High-pressure hydrocracking unit shutdown

[0130] In this implementation case, the high-pressure hydrocracking unit is scheduled to shut down from the 6th to the 10th day of the scheduling period, for a total of 5 days. Among them, before and after the shutdown, the high-pressure hydrocracking unit pushed its load to the maximum, with the aim of processing as much raw material as possible before the shutdown and reducing the backlog of raw materials during the shutdown. At the same time, this strategy also helped to reserve necessary intermediate products and provided support for the continuous operation of downstream units. In terms of inventory management, light hydrocarbon recovery naphtha and refined oil were stored before the shutdown to prepare for the demand during the shutdown, and then gradually consumed during the shutdown until they approached the initial liquid level, ensuring that normal production could be quickly resumed after the shutdown. The inventory of reforming materials was also consumed during the shutdown and then gradually replenished after the resumption of work, which reflects the impact of the shutdown on the supply of raw materials for the reforming unit and the corresponding recovery measures. The trend of changes in wax oil inventory is similar, with a decrease before the shutdown, an increase during the shutdown, and a gradual recovery after the shutdown, ensuring the continuous supply of wax oil as an important raw material.

[0131] It can be concluded that when facing planned shutdowns of different units such as delayed coking and high-pressure hydrocracking, through fine load distribution and inventory management, such as dynamically adjusting unit loads, reserving raw materials in advance, and coordinating the operation of upstream and downstream units, the impact of shutdowns, maintenance, and defect elimination on the overall production process can be minimized. By increasing production and reserving raw materials in advance, upstream production is ensured to be stable during the shutdown, the supply and demand of materials are balanced, and the risk of chain shutdowns is avoided. At the same time, the inventory scheduling and control of key intermediate materials ensures that the inventory before and after the shutdown is appropriate, avoiding excess or shortage of inventory, and production can be resumed quickly after the shutdown.

[0132] This specification also provides a computer-readable medium storing computer program code, which, when executed by a processor, implements the above-mentioned attribute-based method for optimizing a planned shutdown scheduling scheme for a refining production unit.

[0133] This specification also provides an attribute-based optimization method for a planned shutdown scheduling scheme for a petrochemical production unit, including an instruction memory for storing instructions executable by a processor, and a processor for executing instructions in the instruction memory to implement the attribute-based optimization method for a planned shutdown scheduling scheme for a petrochemical production unit as described above.

[0134] The previous description of the disclosure is provided to enable any person skilled in the art to make or use the disclosure. Various modifications to the 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 the disclosure. Thus, the disclosure is not intended to be limited to the examples and designs described herein, but should be granted the widest scope consistent with the principles and novel features disclosed herein.

[0135] 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 may be implemented as electronic hardware, computer software, or a combination of the two. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps are generally described above 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. The technician may implement the described functionality in different ways for each specific application, but such implementation decisions should not be interpreted as resulting in a departure from the scope of the present invention.

[0136] The various illustrative logic blocks, modules, and circuits described in conjunction with the embodiments disclosed herein may be implemented or performed with a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in cooperation with a DSP core, or any other such configuration.

[0137] The steps of the method or algorithm 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 the two. The software module may reside in a RAM memory, a flash memory, a ROM memory, an EPROM memory, an EEPROM memory, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor so that the processor can read and write information from / to the storage medium. In an alternative, a storage medium may be integrated into a processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In an alternative, the processor and the storage medium may reside in a user terminal as discrete components.

[0138] In one or more exemplary embodiments, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented as a computer program product in software, each function may be stored on or transmitted by a computer-readable medium as one or more instructions or codes. Computer-readable media include both computer storage media and communication media, including any medium that facilitates the transfer of a computer program from one place to another. Storage media may be any available medium that can be accessed by a computer. As an example and not limitation, such a computer-readable medium may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, disk storage or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of an instruction or data structure and can be accessed by a computer. Any connection is also properly referred to as a computer-readable medium. For example, if the software is transmitted from a website, a server, or other remote source using a coaxial cable, a fiber optic cable, a twisted pair, a digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwaves, the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwaves are included in the definition of the medium. Disk and disc as used herein include compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray disc, wherein disk often reproduces data magnetically, while disc reproduces data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.

Claims

1. A method for optimizing a planned shutdown scheduling scheme for a refining and chemical production unit, characterized in that: The following steps are involved: Step S1: Collect production equipment information and material inventory information according to the actual production process of the refinery; Step S2: constructing a basic scheduling model under the normal operation state of the refinery based on the collected production unit information and material inventory information; Step S3: Solve the basic scheduling model according to the processing device information and the material inventory information to obtain the load benchmark value, material flow benchmark value and material inventory benchmark value of each production device; Step S4: Based on the load benchmark value, material trend benchmark value and material inventory benchmark value of each production unit obtained by solving, combined with the planned shutdown arrangement of the refinery, a planned shutdown scheduling optimization model for the production unit is constructed; Step S5: Solve the production unit planned shutdown scheduling optimization model to generate a planned shutdown scheduling plan, thereby completing the optimization of the production unit scheduling plan under the planned shutdown state.

2. The method for optimizing the planned shutdown scheduling scheme for a refining and chemical production unit according to claim 1, characterized in that: The production unit information includes the information of the atmospheric and vacuum units and the secondary processing units involved in the production, and the material inventory information includes the raw material information, the intermediate material information, and the product information. The planned shutdown scheduling plan for the refining and chemical production units constructs a basic scheduling model under the normal operation state of the simulated refinery based on the collected production unit information and material inventory information.

3. The method for optimizing the scheduled shutdown scheduling scheme for a refining and chemical production unit according to claim 2, characterized in that: After obtaining the production unit information and material inventory information, the optimization method for the planned shutdown scheduling scheme for coping with the refining production unit establishes the constraints of the basic scheduling model based on the obtained production unit information; wherein the constraints of the basic scheduling model include: unit processing capacity constraints, unit material balance constraints, inventory in-and-out balance constraints, inventory upper and lower limit constraints, raw material supply constraints, and unit feed ratio constraints.

4. The method for optimizing the scheduled shutdown scheduling scheme for a refining and chemical production unit according to claim 3, characterized in that: The device processing capacity constraints are as follows: Among them, U represents the set of production devices, Cap u,i represents the load of production unit u on day i, represents the upper limit of the device load of production device u, Indicates the lower limit of the device load of production device u.

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

6. The method for optimizing the scheduled shutdown scheduling scheme for a refining and chemical production unit according to claim 5, characterized in that: The material balance constraints include secondary device material balance constraints and diversion device material balance constraints. The method for optimizing the planned shutdown scheduling scheme for a refining production unit uses a material balance model to perform secondary device material balance constraints and diversion device material balance constraints on a 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 the scheduled shutdown scheduling scheme for a refining and chemical production unit according to claim 6, characterized in that: The material balance constraints for the secondary device are as follows: Where U represents the set of production devices, Cap u,i represents the production load of production unit u on day i, yield u,s represents the yield of each output stream s of production unit u, Mass s,i Represents the flow rate of each stream s of production unit u on the i-th day.

8. The method for optimizing the scheduled shutdown scheduling scheme for a refining and chemical production unit according to claim 6, characterized in that: The material balance constraints of the splitter are as follows: Where U represents the set of production devices, U SPL Represents a collection of diversion devices, Represents the feed collection of the splitter, Indicates the discharging collection of the diversion device, Mass s,i Represents the flow rate of each stream s in the diversion device on the i-th day.

9. The method for optimizing the scheduled shutdown scheduling scheme for a refining and chemical production unit according to claim 3, characterized in that: The inventory inflow and outflow balance constraints are as follows: Among them, Inv t,i represents the inventory of inventory t on day i, A collection of stock feeds, Represents a collection of outgoing materials from inventory. Inv t,i represents the liquid level of inventory t on day i, Inv t,i-1 represents the inventory level t on day i-1.

10. The method for optimizing the scheduled shutdown scheduling scheme for a refining and chemical production unit according to claim 3, characterized in that: The upper and lower inventory constraints are as follows: in, represents the upper limit of the tank capacity of inventory t, Represents the lower limit of the tank storage capacity of inventory t.

11. The method for optimizing a planned shutdown scheduling scheme for a refining and chemical production unit according to claim 3, characterized in that: After determining the constraints of the basic scheduling model, the optimization method for the planned shutdown scheduling scheme for the refining production unit is used to solve the basic scheduling model with the minimum load fluctuation of each production unit as the optimization goal and the unchanged material inventory as the special constraint, so as to obtain the load reference value, material trend reference value and material inventory reference value of each production unit; wherein the optimization goal is as follows: Among them, Obj represents the optimization target value of the basic scheduling model, Cap u,i represents the production load of production unit u on day i, Cap u,0 Indicates the set value of the device load, λ1 represents the device load deviation weight coefficient, μ1 represents the device load switching penalty coefficient. N u Indicates the number of load switching times of the device.

12. The method for optimizing a planned shutdown scheduling scheme for a refining and chemical production unit according to claim 1, characterized in that: The method for optimizing the planned shutdown scheduling scheme for a refining production unit introduces a binary variable ∈ 1 when constructing a planned shutdown scheduling optimization model for a production unit. u and daily changes in inventory are included in the state binary variable p t To constrain; among them, If the device shutdown state binary variable ∈ u If the value is "1", it means that the production unit u is running, and if it is "0", it means that the production unit u is in a planned shutdown state; If the daily change in inventory is included in the state binary variable p t If the value is "1", it means that the daily change of inventory t is included. If it is "0", it means that the daily change of inventory t is not included.

13. The method for optimizing a planned shutdown scheduling scheme for a refining and chemical production unit according to claim 1, characterized in that: The planned shutdown scheduling optimization model for production units has multiple optimization objectives, including: the optimization objectives include minimizing the number of unit load adjustments, minimizing the change between the beginning and the end of the inventory period, minimizing the daily change in inventory, and minimizing the unit shutdown duration; wherein, when solving the planned shutdown scheduling optimization model for refining and chemical production units, the optimization method sets a corresponding weight coefficient according to the importance of each optimization objective, and converts multiple objectives into a single objective in a coefficient weighted form for optimization, thereby obtaining the final solution of the planned shutdown scheduling optimization model for production units.

14. The method for optimizing a planned shutdown scheduling scheme for a refining and chemical production unit according to claim 1, characterized in that: The optimization objectives of the planned shutdown scheduling optimization model for production units are as follows: Wherein, Obj represents the optimization target value of the planned shutdown scheduling optimization model of the production unit, Cap u,i Cap u,i represents the load of production unit u on day i, Cap u,0 Cap u,i represents the initial value of the production unit u load, N u Indicates the number of load switching times of the device. ∈ u A binary variable value representing the shutdown status of the device, Inv t,i represents the liquid level of inventory t on day i, Int t,0 represents the initial value of the inventory level on day i-1, represents the maximum liquid level of inventory t on day i, μ2 represents a shutdown penalty coefficient, λ1, μ1, λ2, and λ3 are the weight coefficients of the corresponding parameters.

15. A computer readable medium storing computer program code, characterized in that: The computer program code implements the method according to any one of claims 1 to 14 when executed by a processor.

16. A method and device for optimizing a planned shutdown scheduling scheme for a refining production unit, characterized in that: include: a memory for storing instructions executable by a processor; as well as A processor, configured to execute the instructions to implement the method according to any one of claims 1 to 14.

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