A production collaborative scheduling method and system based on a multi-level petrochemical whole plant structure
By constructing a production and consumption model and a scheduling planning model based on a multi-level petrochemical plant structure, the problems of unclear hierarchical relationships and deep coupling in petrochemical production scheduling were solved, and more accurate and flexible production collaborative scheduling was achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SINOPEC ENERGY SAVING TECH SERVICE CO LTD
- Filing Date
- 2026-02-12
- Publication Date
- 2026-06-09
AI Technical Summary
Existing petrochemical production scheduling methods fail to effectively address the unclear hierarchical relationship between the plant's production system and utility systems, leading to distorted calculations of material and energy flow connections, increased complexity of optimization models, and neglect of the deep coupling between material and energy flows. This results in inaccurate scheduling schemes that are difficult to adapt to changes in multiple operating conditions.
Based on the multi-level petrochemical plant structure, the operation data of each production unit under multiple preset operating conditions are collected to construct a production and consumption model, which characterizes the relationship between energy medium production and consumption and the processing volume of the production unit. Combining the maximization of the scheduling profit of the entire plant system as the optimization objective, a scheduling planning model is constructed, and the optimal scheduling plan is obtained through a solver.
It improves the accuracy and adaptability of scheduling plans, reduces energy waste, and ensures the effectiveness and feasibility of collaborative scheduling of production systems under multiple operating conditions.
Smart Images
Figure CN122175224A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of petrochemical industry production control, and in particular to a production collaborative scheduling method and system based on a multi-level petrochemical plant structure. Background Technology
[0002] The petrochemical industry's production process is characterized by long cycles, multi-unit linkage, and cross-generation and consumption of multiple energy media: on the one hand, the production process involves multiple stages, and the material flow needs to be transferred in an orderly manner between different production units; on the other hand, the energy media such as steam and electricity provided by public works systems such as boilers and steam turbines need to match the energy consumption requirements of each production unit, resulting in a deep coupling between energy flow and material flow.
[0003] Existing methods for optimizing petrochemical production scheduling often overlook the following characteristics: First, the hierarchical relationship between the plant's production system and utilities is unclear, leading to cross-level material / energy flow connections, resulting in calculation distortion, increased complexity of the optimization model, and ultimately, scheduling schemes that fail to meet actual needs. Second, optimization is often performed solely on material or energy flows, ignoring the deep coupling between them, leading to energy waste or production interruptions, and thus inaccurate scheduling schemes. Third, conventional production planning only considers a single operating condition, resulting in incompatibility with multiple operating conditions, leading to rigid and difficult-to-adjust actual scheduling. Therefore, how to further optimize petrochemical production scheduling and improve its accuracy remains a pressing technical problem that needs to be solved by existing technologies. Summary of the Invention
[0004] This application provides a production collaborative scheduling method and system based on a multi-level petrochemical plant structure to solve the technical problem that the accuracy of existing petrochemical production scheduling does not meet actual needs.
[0005] According to a first aspect of the embodiments of this application, a production collaborative scheduling method based on a multi-level petrochemical plant structure is provided, comprising: Based on the hierarchical structure of the entire plant system to be scheduled, collect the operating data of each production unit under multiple preset operating conditions; Based on the operating data of each production unit under multiple preset operating conditions, production consumption models for each production unit under multiple preset operating conditions are constructed respectively; wherein, the production consumption model is used to characterize the relationship between the energy medium production consumption and the processing volume of the production unit under the corresponding preset operating conditions. Based on the operating data of each production unit under multiple preset operating conditions, a scheduling planning model for the entire plant system is constructed with the optimization objective of maximizing the scheduling profit of the entire plant system. The scheduling planning model includes an objective function and multiple operational scheduling constraints. At least one operational scheduling constraint is constructed based on the production consumption model of each production unit under multiple preset operating conditions. Solve the scheduling planning model to obtain the scheduling plan for the entire plant system, and coordinate the production of the entire plant system according to the scheduling plan.
[0006] This application first collects operational data of each production unit under multiple preset operating conditions based on the hierarchical structure of the entire plant system. Then, it constructs a production consumption model for each production unit under multiple preset operating conditions. This model can clearly define and refine the data flow to be collected through the hierarchical structure, thereby constructing a more accurate production consumption model. At the same time, the production consumption model characterizes the relationship between energy medium production consumption and the processing volume of the production unit under the corresponding preset operating conditions. It can characterize the coupling relationship between material flow and energy flow and distinguish the coupling relationship under different operating conditions, thereby constructing a more accurate production consumption model. With the goal of maximizing the scheduling profit of the entire plant system, the scheduling planning model constructed through the production consumption model has a higher degree of matching with the entire plant system. Therefore, the scheduling plan obtained by solving the scheduling planning model is more accurate. That is, the accuracy of the coordinated production scheduling of the entire plant system based on the scheduling plan is higher, so as to meet the actual needs.
[0007] In some embodiments of this application, the step of constructing a production consumption model for each production unit under multiple preset operating conditions based on the operating data of each production unit under multiple preset operating conditions specifically includes: Based on the operating data of each production unit under multiple preset operating conditions, the consumption of various preset energy media is modeled to characterize the relationship between the energy media consumption and the processing capacity of the production unit, and the first production and consumption model of each production unit for each energy media under multiple preset operating conditions is constructed. Based on the operating data of each production unit under multiple preset operating conditions, the output of multiple preset energy media is modeled to characterize the relationship between the output of energy media and the processing volume of the production unit, and a second production and consumption model for each energy media is constructed for each production unit under multiple preset operating conditions. Based on the first and second production consumption models of each production unit under multiple preset operating conditions for each energy medium, the production consumption models of each production unit under multiple preset operating conditions are obtained.
[0008] This application first models the consumption and output of various energy media based on the operating data of each production unit under multiple preset operating conditions, in order to characterize the relationship between the consumption or output of energy media and the processing capacity of the production unit. Then, it constructs the production consumption model of each production unit under multiple preset operating conditions. By characterizing the relationship between the production consumption of energy media and the processing capacity of the production unit under the corresponding preset operating conditions, the production consumption model can characterize the coupling relationship between material flow and energy flow, and distinguish the coupling relationship under different operating conditions and different energy media, thereby constructing a more accurate production consumption model. As a result, the scheduling planning model constructed by the production consumption model is more compatible with the entire plant system, and the solved scheduling plan is more accurate. The accuracy of collaborative scheduling based on the solved scheduling plan is also higher.
[0009] In some embodiments of this application, the step of constructing a scheduling planning model for the entire plant system based on the operating data of each production unit under multiple preset operating conditions, with the optimization objective of maximizing the scheduling profit of the entire plant system, specifically includes: Based on the operating data of each production unit under multiple preset operating conditions, the material flow income and expenditure, energy flow income and expenditure, and equipment loss of the entire plant system are modeled. The objective function for scheduling planning is initialized with the goal of maximizing the scheduling profit of the entire plant system. Based on the operating data of each production unit under multiple preset operating conditions, and combined with the production and consumption models of each production unit under multiple preset operating conditions, the material flow constraints, unit operation constraints, unit production and consumption constraints and unit cost constraints of the entire plant system are modeled, and multiple operation scheduling constraints of the objective function are set. Based on the objective function and the multiple operational scheduling constraints, a scheduling planning model for the entire plant system is constructed.
[0010] This application first models the material flow inflow and outflow, energy flow inflow and outflow, and equipment loss of the entire plant system based on the operating data of each production unit under multiple preset operating conditions. The objective function is initialized with the goal of maximizing scheduling profit, which quantifies production scheduling optimization into scheduling profit, simplifying model description and solution complexity. Furthermore, by maintaining the separate modeling of material flow inflow and outflow and equipment loss, confusion between the modeling of material flow and energy flow is avoided, resulting in an objective function that is more suitable for the scheduling planning of the entire plant system. Simultaneously, by combining the production and consumption models of each production unit under multiple preset operating conditions, constraints on the objective function are modeled from multiple perspectives. Multi-dimensional constraints ensure the feasibility of the model, making the scheduling planning model more suitable for the scheduling planning of the entire plant system.
[0011] In some embodiments of this application, the material flow constraints include variable boundary constraints, inventory constraints, and non-inventory constraints; the device operation constraints include device processing capacity constraints, device single-mode processing capacity constraints, device processing capacity balance constraints, device mode quantity constraints, blending device constraints, and steam balance constraints.
[0012] This application ensures the feasibility of the model by constructing multi-dimensional constraints on material flow and equipment operation, thereby making the scheduling planning model more compatible with the scheduling planning of the entire plant system.
[0013] In some embodiments of this application, the device production and consumption constraints include device material production and consumption balance constraints, device single-mode material production and consumption balance constraints, device yield balance constraints, device mode change constraints, and device production and consumption model constraints.
[0014] This application ensures the feasibility of the model by constructing a multi-dimensional framework for the constraints on the production and consumption of the equipment, thereby making the scheduling planning model more compatible with the scheduling planning of the entire plant system.
[0015] In some embodiments of this application, solving the scheduling planning model to obtain the scheduling plan for the entire plant system specifically includes: Based on a preset solver, the scheduling planning model is solved at a preset convergence accuracy to obtain the scheduling plan for the entire plant system; wherein, the scheduling plan includes the optimal processing volume of each production unit, the corresponding preset operating conditions, and the allocation of energy medium.
[0016] This application solves the scheduling planning model under the conditions of preset solver and convergence accuracy to obtain a scheduling plan that includes the optimal processing volume of each production unit, preset operating conditions and energy medium allocation. It can accurately solve the scheduling planning model, thereby making the production coordination scheduling of the whole plant system more accurate according to the scheduling plan and meeting actual needs.
[0017] In some embodiments of this application, the step of coordinating the production of the entire plant system according to the scheduling plan further includes: Real-time production data is collected to coordinate the production of the entire plant system according to the scheduling plan, and the production of the entire plant system is predicted based on the scheduling plan to obtain predicted production data. The real-time production data and the predicted production data are uploaded to the plant-wide system for visualization.
[0018] This application first collects real-time production data and predicted production data obtained by forecasting production collaborative scheduling based on the scheduling plan, and then uploads them for visualization display, which can intuitively show the collaborative scheduling results of the scheduling plan.
[0019] According to a second aspect of the embodiments of this application, a production collaborative scheduling system based on a multi-level petrochemical plant structure is provided, including a hierarchical data acquisition module, a production and consumption model construction module, a scheduling model construction module, and a model solving scheduling module; The hierarchical data acquisition module is used to collect the operating data of each production unit under multiple preset operating conditions according to the hierarchical structure of the entire plant system to be scheduled. The production consumption model construction module is used to construct production consumption models for each production unit under multiple preset operating conditions based on the operating data of each production unit under multiple preset operating conditions; wherein, the production consumption model is used to characterize the relationship between the energy medium production consumption and the processing volume of the production unit under the corresponding preset operating conditions. The scheduling model construction module is used to construct a scheduling planning model for the entire plant system based on the operating data of each production unit under multiple preset operating conditions, with the optimization objective of maximizing the scheduling profit of the entire plant system; wherein, the scheduling planning model includes an objective function and multiple operational scheduling constraints; at least one operational scheduling constraint is constructed based on the production consumption model of each production unit under multiple preset operating conditions; The model solving and scheduling module is used to solve the scheduling planning model, obtain the scheduling plan for the entire plant system, and coordinate the production of the entire plant system according to the scheduling plan.
[0020] In some embodiments of this application, the production and consumption model construction module includes a first model construction unit, a second model construction unit, and a production and consumption model construction unit; The first model building unit is used to model the consumption of a variety of preset energy media based on the operating data of each production device under multiple preset operating conditions, so as to characterize the relationship between the energy media consumption and the processing volume of the production device, and to build the first production and consumption model of each production device for each energy medium under multiple preset operating conditions. The second model building unit is used to model the output of a variety of preset energy media based on the operating data of each production device under multiple preset operating conditions, so as to characterize the relationship between the output of energy media and the processing volume of the production device, and to build a second production and consumption model for each energy medium under multiple preset operating conditions for each production device. The production consumption model construction unit is used to obtain the production consumption model of each production device under multiple preset operating conditions based on the first and second production consumption models of each energy medium under multiple preset operating conditions.
[0021] In some embodiments of this application, the scheduling model construction module includes an objective function initialization unit, a constraint modeling setting unit, and a scheduling model construction unit; The objective function initialization unit is used to model the material flow income and expenditure, energy flow income and expenditure, and equipment loss of the entire plant system based on the operating data of each production unit under multiple preset operating conditions, and initialize the objective function for scheduling planning with the optimization objective of maximizing the scheduling profit of the entire plant system. The constraint modeling setting unit is used to model the material flow constraints, device operation constraints, device production and consumption constraints and device cost constraints of the whole plant system based on the operating data of each production device under multiple preset operating conditions and the production and consumption model of each production device under multiple preset operating conditions, and to set multiple operation scheduling constraints of the objective function. The scheduling model construction unit is used to construct the scheduling planning model of the entire plant system based on the objective function and the multiple operational scheduling constraints.
[0022] In some embodiments of this application, the material flow constraints include variable boundary constraints, inventory constraints, and non-inventory constraints; the device operation constraints include device processing capacity constraints, device single-mode processing capacity constraints, device processing capacity balance constraints, device mode quantity constraints, blending device constraints, and steam balance constraints.
[0023] In some embodiments of this application, the device production and consumption constraints include device material production and consumption balance constraints, device single-mode material production and consumption balance constraints, device yield balance constraints, device mode change constraints, and device production and consumption model constraints.
[0024] In some embodiments of this application, the model solving scheduling module includes a scheduling plan solving unit; the scheduling plan solving unit is used to solve the scheduling planning model based on a preset solver and at a preset convergence accuracy to obtain the scheduling plan of the entire plant system; wherein, the scheduling plan includes the optimal processing volume of each production unit, the corresponding preset operating conditions, and the energy medium allocation.
[0025] In some embodiments of this application, the model solving scheduling module includes a data acquisition and prediction unit and a data visualization unit; The data acquisition and prediction unit is used to acquire real-time production data for the coordinated scheduling of the production of the entire plant system according to the scheduling plan, and to predict the production of the entire plant system based on the scheduling plan to obtain predicted production data. The data visualization unit is used to upload the real-time production data and the predicted production data to the plant-wide system for visualization display.
[0026] This application first collects operational data of each production unit under multiple preset operating conditions based on the hierarchical structure of the entire plant system. Then, it constructs a production consumption model for each production unit under multiple preset operating conditions. This model can clearly define and refine the data flow to be collected through the hierarchical structure, thereby constructing a more accurate production consumption model. At the same time, the production consumption model characterizes the relationship between energy medium production consumption and the processing volume of the production unit under the corresponding preset operating conditions. It can characterize the coupling relationship between material flow and energy flow and distinguish the coupling relationship under different operating conditions, thereby constructing a more accurate production consumption model. With the goal of maximizing the scheduling profit of the entire plant system, the scheduling planning model constructed through the production consumption model has a higher degree of matching with the entire plant system. Therefore, the scheduling plan obtained by solving the scheduling planning model is more accurate. That is, the accuracy of the coordinated production scheduling of the entire plant system based on the scheduling plan is higher, so as to meet the actual needs. Attached Figure Description
[0027] Figure 1 This is a flowchart illustrating a production collaborative scheduling method based on a multi-level petrochemical plant structure, as shown in certain embodiments of this application. Figure 2 This is a module structure diagram of a production collaborative scheduling system based on a multi-level petrochemical plant structure, as shown in some embodiments of this application. Detailed Implementation
[0028] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below in conjunction with the accompanying drawings are exemplary and are only used to explain some embodiments of this application, and should not be construed as limiting the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments shown in this application without inventive effort are within the protection scope of this application.
[0029] In the description of this application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, unless otherwise explicitly specified, "a plurality of" or "several" means two or more.
[0030] Existing methods for optimizing petrochemical production scheduling suffer from several unresolved shortcomings: First, the hierarchical relationship between the plant's production system and utilities is unclear, leading to cross-level material / energy flow connections, resulting in calculation distortion, increased complexity of the optimization model, and ultimately, scheduling schemes that fail to meet actual needs. Second, optimization often focuses solely on material or energy flows, neglecting their deep coupling, leading to energy waste or production interruptions, and consequently, inaccurate scheduling schemes. Third, conventional production planning considers only a single operating condition, exhibiting incompatibility with multiple operating conditions, resulting in rigid and difficult-to-adjust actual scheduling. Therefore, how to further optimize petrochemical production scheduling and improve its accuracy remains a pressing technical problem that needs to be addressed by existing technologies.
[0031] Based on the above technical background, please refer to Figure 1 This application provides a production collaborative scheduling method based on a multi-level petrochemical plant structure, including steps S101 to S104, each step as follows: Step S101: Based on the hierarchical structure of the entire plant system to be scheduled, collect the operating data of each production unit under multiple preset operating conditions.
[0032] Specifically, the hierarchical structure of the entire plant system to be scheduled comprises three levels. The first level consists of the refining and chemical production areas, each operating independently and possessing a complete production system and utility system. The second level comprises the production systems and utility systems of each production area. The production systems include a complete process flow encompassing raw material handling, processing conversion, and product refining, while the utility systems provide energy media such as steam and electricity and receive waste heat from the production systems. The third level comprises the individual production units (such as crude oil atmospheric and vacuum distillation units and catalytic cracking units) under each production system or the unit equipment (such as boilers and steam turbines) under each utility system, including the network connections of material and energy flows between units or equipment. Material and energy flows can only be connected or transferred between adjacent levels; cross-level connections or transfers are not possible. Generally, the hierarchical structure of the entire plant system has at least three levels, and can have four or more, with the number of levels depending on the granularity of the entire plant system.
[0033] Specifically, each production unit can only switch between several preset operating conditions, and there is no situation where the actual operating condition does not match the preset operating condition; each production unit is equipped with at least two preset operating conditions, namely high-load or low-load conditions; the number of preset operating conditions can be changed according to different production units, different production needs, or different configurations of the entire plant system. The two preset operating conditions in this application are only for illustrative purposes and do not constitute a limitation on the number of preset operating conditions or specific configurations; the production unit includes processing units. , blending device and public works equipment .
[0034] By collecting operational data from various production units within the plant system under multiple preset operating conditions through the hierarchical structure of the entire plant system, the scope of the hierarchy can be clearly defined through the hierarchical structure and preset operating conditions, and the connection between material flow and energy flow can be sorted out, thereby clarifying the data flow to be collected and providing a data foundation for the subsequent construction of production and consumption models.
[0035] Step S102: Based on the operating data of each production unit under multiple preset operating conditions, construct the production consumption model of each production unit under multiple preset operating conditions; wherein, the production consumption model is used to characterize the relationship between the energy medium production consumption and the processing volume of the production unit under the corresponding preset operating conditions.
[0036] In some embodiments of this application, the step of constructing a production consumption model for each production unit under multiple preset operating conditions based on the operating data of each production unit under multiple preset operating conditions specifically includes: Based on the operating data of each production unit under multiple preset operating conditions, the consumption of various preset energy media is modeled to characterize the relationship between the energy media consumption and the processing capacity of the production unit, and the first production and consumption model of each production unit for each energy media under multiple preset operating conditions is constructed. Based on the operating data of each production unit under multiple preset operating conditions, the output of multiple preset energy media is modeled to characterize the relationship between the output of energy media and the processing volume of the production unit, and a second production and consumption model for each energy media is constructed for each production unit under multiple preset operating conditions. Based on the first and second production consumption models of each production unit under multiple preset operating conditions for each energy medium, the production consumption models of each production unit under multiple preset operating conditions are obtained.
[0037] Specifically, the first production and consumption model of each production unit for each energy medium under multiple preset operating conditions can be summarized into the energy medium consumption equation of the unit, as follows: ; Similarly, the second production and consumption model for each energy medium under multiple preset operating conditions of each production unit can be summarized into the unit's energy production medium equation, specifically: ; in, They represent the production units respectively. Under working conditions ,cycle The energy medium below Consumption and output; They represent the production units respectively. Under working conditions ,cycle The energy medium below The actual unit consumption and actual output coefficient; They represent the production units respectively. Under working conditions ,cycle Energy medium during shutdown The actual unit consumption and actual output coefficient; Indicates production equipment Under working conditions ,cycle The processing volume below; A collection of processing devices; These represent the power generation and consumption collection, the steam generation and consumption collection of 3.5 MPa, and the steam generation and consumption collection of 1.0 MPa, respectively.
[0038] This application first models the consumption and output of various energy media based on the operating data of each production unit under multiple preset operating conditions, in order to characterize the relationship between the consumption or output of energy media and the processing capacity of the production unit. Then, it constructs the production consumption model of each production unit under multiple preset operating conditions. By characterizing the relationship between the production consumption of energy media and the processing capacity of the production unit under the corresponding preset operating conditions, the production consumption model can characterize the coupling relationship between material flow and energy flow, and distinguish the coupling relationship under different operating conditions and different energy media, thereby constructing a more accurate production consumption model. As a result, the scheduling planning model constructed by the production consumption model is more compatible with the entire plant system, and the solved scheduling plan is more accurate. The accuracy of collaborative scheduling based on the solved scheduling plan is also higher.
[0039] Step S103: Based on the operating data of each production unit under multiple preset operating conditions, construct a scheduling planning model for the entire plant system with the optimization objective of maximizing the scheduling profit of the entire plant system; wherein, the scheduling planning model includes an objective function and multiple operating scheduling constraints; at least one operating scheduling constraint is constructed based on the production consumption model of each production unit under multiple preset operating conditions.
[0040] In some embodiments of this application, the step of constructing a scheduling planning model for the entire plant system based on the operating data of each production unit under multiple preset operating conditions, with the optimization objective of maximizing the scheduling profit of the entire plant system, specifically includes: Based on the operating data of each production unit under multiple preset operating conditions, the material flow income and expenditure, energy flow income and expenditure, and equipment loss of the entire plant system are modeled. The objective function for scheduling planning is initialized with the goal of maximizing the scheduling profit of the entire plant system. Based on the operating data of each production unit under multiple preset operating conditions, and combined with the production and consumption models of each production unit under multiple preset operating conditions, the material flow constraints, unit operation constraints, unit production and consumption constraints and unit cost constraints of the entire plant system are modeled, and multiple operation scheduling constraints of the objective function are set. Based on the objective function and the multiple operational scheduling constraints, a scheduling planning model for the entire plant system is constructed.
[0041] Specifically, the objective function can be expressed in the following form: ; The first item represents product sales revenue; the second item represents the sales revenue of public works energy media from the system's external sales; the third and fourth items represent raw material purchase costs and material inventory costs, respectively; the fifth item represents operating costs caused by changes in the operating conditions of the production equipment; the sixth item represents the cost of public works energy media consumed by the system; and the last item represents fixed equipment costs. These represent the product subset, raw material subset, and inventory subset, respectively. Indicates product or raw material The price Indicates product Inventory costs, Indicates period Products sold internally Quantity, Indicates period Purchased raw materials Quantity, Indicates period Domestic products Inventory levels Indicates production equipment Under working conditions ,cycle Internally produced energy medium Quantity, Indicates production equipment Under working conditions ,cycle Internal energy consumption medium Quantity; Indicates production equipment From working conditions To working condition Change costs, It is a variable between 0 and 1, and a value of 1 indicates a production device. In the cycle Internal working conditions Changes in operating conditions Otherwise, it is 0; This represents the cost of fixed equipment.
[0042] This application first models the material flow inflow and outflow, energy flow inflow and outflow, and equipment loss of the entire plant system based on the operating data of each production unit under multiple preset operating conditions. The objective function is initialized with the goal of maximizing scheduling profit, which quantifies production scheduling optimization into scheduling profit, simplifying model description and solution complexity. Furthermore, by maintaining the separate modeling of material flow inflow and outflow and equipment loss, confusion between the modeling of material flow and energy flow is avoided, resulting in an objective function that is more suitable for the scheduling planning of the entire plant system. Simultaneously, by combining the production and consumption models of each production unit under multiple preset operating conditions, constraints on the objective function are modeled from multiple perspectives. Multi-dimensional constraints ensure the feasibility of the model, making the scheduling planning model more suitable for the scheduling planning of the entire plant system.
[0043] In some embodiments of this application, the material flow constraints include variable boundary constraints, inventory constraints, and non-inventory constraints; the device operation constraints include device processing capacity constraints, device single-mode processing capacity constraints, device processing capacity balance constraints, device mode quantity constraints, blending device constraints, and steam balance constraints.
[0044] Specifically, in the material flow constraint, the variable boundary constraint is as follows: ; ; ; in, Indicates the upper and lower limits for raw material purchases; Indicates the upper and lower limits of product sales; Indicates the upper and lower limits of product inventory; Represents a collection of matter; The inventory constraints are specifically as follows: ; in, They represent the production units respectively. Under working conditions ,cycle Domestically produced products and consumed materials Quantity; The non-inventory constraint is specifically: ; in, This represents a non-inventory subset.
[0045] Specifically, the processing quantity constraint of the device in the device operation constraint is as follows: ; in, Indicates production equipment In the cycle Internal processing volume; Indicates production equipment The upper and lower limits of processing volume; It is a variable between 0 and 1, and a value of 1 indicates a production device. In the cycle If the value is 0, it indicates that the program is not running; otherwise, a value of 0 indicates that the program is not running. The single-mode processing volume constraint of the device is specifically as follows: ; in, Indicates production equipment Under working conditions Upper and lower limits of hourly processing volume; It is a variable between 0 and 1, and a value of 1 indicates a production device. In the cycle Internal working conditions Run, otherwise 0 indicates not based on operating conditions. run; The processing quantity balance constraint of the device is specifically as follows: ; in, Indicates production equipment Under working conditions ,cycle Internal processing volume; The constraint on the number of device modes is as follows: ; in, Indicates production equipment In the cycle The maximum number of operating modes (maximum number of operating conditions) within the range. The constraints on the blending device include material balance constraints and performance constraints. Specifically, the material balance constraints are as follows: ; in, These are the feed subset and the product subset, respectively; The performance constraints of the harmonizing device are specifically as follows: ; in, Indicates material Properties, Indicates material The set of all attributes; The steam balance constraints include boiler steam production and consumption constraints, turbine steam production and consumption balance constraints, and turbine energy balance constraints. Specifically, the boiler steam production and consumption constraints are as follows: ; in, Indicates public works equipment Under working conditions ,cycle Domestically produced products Quantity; Indicates public works equipment Efficiency; Representing materials ,product And the enthalpy value of boiler feedwater; Indicates public works equipment Consumable materials Quantity; The turbine steam production and consumption balance constraint is specifically as follows: ; The turbine energy balance constraint is specifically as follows: ; in, Indicates public works equipment Production materials Quantity; Indicates material Enthalpy value; These represent public works equipment. Consumable materials and The quantity.
[0046] This application ensures the feasibility of the model by constructing multi-dimensional constraints on material flow and equipment operation, thereby making the scheduling planning model more compatible with the scheduling planning of the entire plant system.
[0047] In some embodiments of this application, the device production and consumption constraints include device material production and consumption balance constraints, device single-mode material production and consumption balance constraints, device yield balance constraints, device mode change constraints, and device production and consumption model constraints.
[0048] Specifically, the device's material production and consumption balance constraint is as follows: ; The single-mode material production and consumption balance constraint of the device is specifically as follows: ; in, Indicates production equipment Under working conditions Time materials The yield; The yield balance constraint of the device is specifically as follows: ; in, Indicates production equipment materials The yield; The device mode change constraint is specifically as follows: ; in, It is a variable between 0 and 1, and a value of 1 indicates a production device. In the cycle Internal working conditions Run, otherwise 0 indicates not based on operating conditions. run; ; in, It is a variable between 0 and 1, and a value of 1 indicates a production device. In the cycle Internal working conditions Run, otherwise 0 indicates not based on operating conditions. run; The constraints of the device's energy consumption model are specifically defined as directly using the energy consumption medium equation and the energy production medium equation of the device as corresponding constraints.
[0049] Specifically, the cost constraint of the device is as follows: ; in, They represent the production units respectively. In the cycle The cost constant and cost coefficient; Indicates production equipment In the cycle Internal processing volume; For the collection of devices, It is a periodic set.
[0050] This application ensures the feasibility of the model by constructing a multi-dimensional framework for the constraints on the production and consumption of the equipment, thereby making the scheduling planning model more compatible with the scheduling planning of the entire plant system.
[0051] Step S104: Solve the scheduling planning model to obtain the scheduling plan for the entire plant system, and coordinate the production of the entire plant system according to the scheduling plan.
[0052] In some embodiments of this application, solving the scheduling planning model to obtain the scheduling plan for the entire plant system specifically includes: Based on a preset solver, the scheduling planning model is solved at a preset convergence accuracy to obtain the scheduling plan for the entire plant system; wherein, the scheduling plan includes the optimal processing volume of each production unit, the corresponding preset operating conditions, and the allocation of energy medium.
[0053] Specifically, the preset solver can be a linear programming solver or a mixed-integer programming solver, such as the Gurobi solver or the CPLEX solver; the preferred value for the preset convergence accuracy is 10. -4 .
[0054] This application solves the scheduling planning model under the conditions of preset solver and convergence accuracy to obtain a scheduling plan that includes the optimal processing volume of each production unit, preset operating conditions and energy medium allocation. It can accurately solve the scheduling planning model, thereby making the production coordination scheduling of the whole plant system more accurate according to the scheduling plan and meeting actual needs.
[0055] In some embodiments of this application, the step of coordinating the production of the entire plant system according to the scheduling plan further includes: Real-time production data is collected to coordinate the production of the entire plant system according to the scheduling plan, and the production of the entire plant system is predicted based on the scheduling plan to obtain predicted production data. The real-time production data and the predicted production data are uploaded to the plant-wide system for visualization.
[0056] This application first collects real-time production data and predicted production data obtained by forecasting production collaborative scheduling based on the scheduling plan, and then uploads them for visualization display, which can intuitively show the collaborative scheduling results of the scheduling plan.
[0057] Compared to existing technologies, this application first collects operational data of each production unit under multiple preset operating conditions based on the hierarchical structure of the entire plant system. Then, it constructs a production consumption model for each production unit under multiple preset operating conditions. This allows for the clear and detailed collection of data streams through the hierarchical structure, resulting in a more accurate production consumption model. Furthermore, the production consumption model characterizes the relationship between energy medium production consumption and the processing volume of the production unit under the corresponding preset operating conditions. It also characterizes the coupling relationship between material flow and energy flow and distinguishes the coupling relationship under different operating conditions, thus constructing a more accurate production consumption model. With the goal of maximizing the scheduling profit of the entire plant system, the scheduling planning model constructed through the production consumption model has a higher degree of matching with the entire plant system. Therefore, the scheduling plan obtained by solving the scheduling planning model is more accurate, meaning that the accuracy of the plant-wide production collaborative scheduling based on the scheduling plan is higher, thus meeting actual needs.
[0058] For a method corresponding to the one described above, please refer to [link to relevant documentation]. Figure 2 The present application provides a production collaborative scheduling system based on a multi-level petrochemical plant structure, including a hierarchical data acquisition module 210, a production and consumption model construction module 220, a scheduling model construction module 230, and a model solving scheduling module 240. The hierarchical data acquisition module 210 is used to collect the operating data of each production unit under multiple preset operating conditions according to the hierarchical structure of the entire plant system to be scheduled. The production consumption model construction module 220 is used to construct production consumption models for each production device under multiple preset operating conditions based on the operating data of each production device under multiple preset operating conditions; wherein, the production consumption model is used to characterize the relationship between the energy medium production consumption and the processing volume of the production device under the corresponding preset operating conditions. The scheduling model construction module 230 is used to construct a scheduling planning model for the entire plant system based on the operating data of each production unit under multiple preset operating conditions, with the optimization objective of maximizing the scheduling profit of the entire plant system; wherein, the scheduling planning model includes an objective function and multiple operational scheduling constraints; at least one operational scheduling constraint is constructed based on the production consumption model of each production unit under multiple preset operating conditions; The model solving scheduling module 240 is used to solve the scheduling planning model to obtain the scheduling plan of the entire plant system, and to coordinate the production of the entire plant system according to the scheduling plan.
[0059] In some embodiments of this application, the production and consumption model construction module 220 includes a first model construction unit, a second model construction unit, and a production and consumption model construction unit; The first model building unit is used to model the consumption of a variety of preset energy media based on the operating data of each production device under multiple preset operating conditions, so as to characterize the relationship between the energy media consumption and the processing volume of the production device, and to build the first production and consumption model of each production device for each energy medium under multiple preset operating conditions. The second model building unit is used to model the output of a variety of preset energy media based on the operating data of each production device under multiple preset operating conditions, so as to characterize the relationship between the output of energy media and the processing volume of the production device, and to build a second production and consumption model for each energy medium under multiple preset operating conditions for each production device. The production consumption model construction unit is used to obtain the production consumption model of each production device under multiple preset operating conditions based on the first and second production consumption models of each energy medium under multiple preset operating conditions.
[0060] In some embodiments of this application, the scheduling model construction module 230 includes an objective function initialization unit, a constraint modeling setting unit, and a scheduling model construction unit; The objective function initialization unit is used to model the material flow income and expenditure, energy flow income and expenditure, and equipment loss of the entire plant system based on the operating data of each production unit under multiple preset operating conditions, and initialize the objective function for scheduling planning with the optimization objective of maximizing the scheduling profit of the entire plant system. The constraint modeling setting unit is used to model the material flow constraints, device operation constraints, device production and consumption constraints and device cost constraints of the whole plant system based on the operating data of each production device under multiple preset operating conditions and the production and consumption model of each production device under multiple preset operating conditions, and to set multiple operation scheduling constraints of the objective function. The scheduling model construction unit is used to construct the scheduling planning model of the entire plant system based on the objective function and the multiple operational scheduling constraints.
[0061] In some embodiments of this application, the material flow constraints include variable boundary constraints, inventory constraints, and non-inventory constraints; the device operation constraints include device processing capacity constraints, device single-mode processing capacity constraints, device processing capacity balance constraints, device mode quantity constraints, blending device constraints, and steam balance constraints.
[0062] In some embodiments of this application, the device production and consumption constraints include device material production and consumption balance constraints, device single-mode material production and consumption balance constraints, device yield balance constraints, device mode change constraints, and device production and consumption model constraints.
[0063] In some embodiments of this application, the model solving scheduling module 240 includes a scheduling plan solving unit; the scheduling plan solving unit is used to solve the scheduling planning model based on a preset solver and at a preset convergence accuracy to obtain the scheduling plan of the entire plant system; wherein, the scheduling plan includes the optimal processing volume of each production unit, the corresponding preset operating conditions, and the energy medium allocation.
[0064] In some embodiments of this application, the model solving scheduling module 240 includes a data acquisition and prediction unit and a data visualization unit; The data acquisition and prediction unit is used to acquire real-time production data for the coordinated scheduling of the production of the entire plant system according to the scheduling plan, and to predict the production of the entire plant system based on the scheduling plan to obtain predicted production data. The data visualization unit is used to upload the real-time production data and the predicted production data to the plant-wide system for visualization display.
[0065] This application first collects operational data of each production unit under multiple preset operating conditions based on the hierarchical structure of the entire plant system. Then, it constructs a production consumption model for each production unit under multiple preset operating conditions. This model can clearly define and refine the data flow to be collected through the hierarchical structure, thereby constructing a more accurate production consumption model. At the same time, the production consumption model characterizes the relationship between energy medium production consumption and the processing volume of the production unit under the corresponding preset operating conditions. It can characterize the coupling relationship between material flow and energy flow and distinguish the coupling relationship under different operating conditions, thereby constructing a more accurate production consumption model. With the goal of maximizing the scheduling profit of the entire plant system, the scheduling planning model constructed through the production consumption model has a higher degree of matching with the entire plant system. Therefore, the scheduling plan obtained by solving the scheduling planning model is more accurate. That is, the accuracy of the coordinated production scheduling of the entire plant system based on the scheduling plan is higher, so as to meet the actual needs.
[0066] It should be understood that the system provided in this application corresponds to the aforementioned method. The production collaborative scheduling system based on a multi-level petrochemical plant structure provided in this application can realize the production collaborative scheduling method based on a multi-level petrochemical plant structure provided in any one of the embodiments of this application.
[0067] Adaptively, embodiments of this application also provide a computer device and a computer-readable storage medium.
[0068] The computer device includes: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor; The processor executes the computer program to implement a production collaborative scheduling method based on a multi-level petrochemical plant structure as described in this application.
[0069] The computer-readable storage medium stores multiple instructions, which are adapted for a processor to load and execute a production collaborative scheduling method based on a multi-level petrochemical plant structure as described in this application.
[0070] The above description represents some embodiments of this application, providing a further detailed explanation of the purpose, technical solution, and beneficial effects of this application. It should be understood that the above-described embodiments of this application should not be construed as limiting this application. In particular, any changes, modifications, equivalent substitutions, and variations made by those skilled in the art within the spirit and principles of this application should be included within the scope of protection of this application.
Claims
1. A production collaborative scheduling method based on a multi-level petrochemical plant structure, characterized in that, include: Based on the hierarchical structure of the entire plant system to be scheduled, collect the operating data of each production unit under multiple preset operating conditions; Based on the operating data of each production unit under multiple preset operating conditions, production consumption models for each production unit under multiple preset operating conditions are constructed respectively; wherein, the production consumption model is used to characterize the relationship between the energy medium production consumption and the processing volume of the production unit under the corresponding preset operating conditions. Based on the operating data of each production unit under multiple preset operating conditions, a scheduling planning model for the entire plant system is constructed with the optimization objective of maximizing the scheduling profit of the entire plant system. The scheduling planning model includes an objective function and multiple operational scheduling constraints. At least one operational scheduling constraint is constructed based on the production consumption model of each production unit under multiple preset operating conditions. Solve the scheduling planning model to obtain the scheduling plan for the entire plant system, and coordinate the production of the entire plant system according to the scheduling plan.
2. The production collaborative scheduling method based on a multi-level petrochemical plant structure according to claim 1, characterized in that, The step of constructing production and consumption models for each production unit under multiple preset operating conditions based on operating data of each production unit under multiple preset operating conditions specifically includes: Based on the operating data of each production unit under multiple preset operating conditions, the consumption of various preset energy media is modeled to characterize the relationship between the energy media consumption and the processing capacity of the production unit, and the first production and consumption model of each production unit for each energy media under multiple preset operating conditions is constructed. Based on the operating data of each production unit under multiple preset operating conditions, the output of multiple preset energy media is modeled to characterize the relationship between the output of energy media and the processing volume of the production unit, and a second production and consumption model for each energy media is constructed for each production unit under multiple preset operating conditions. Based on the first and second production consumption models of each production unit under multiple preset operating conditions for each energy medium, the production consumption models of each production unit under multiple preset operating conditions are obtained.
3. The production collaborative scheduling method based on a multi-level petrochemical plant structure according to claim 1, characterized in that, Based on the operating data of each production unit under multiple preset operating conditions, and with the optimization objective of maximizing the scheduling profit of the entire plant system, a scheduling planning model for the entire plant system is constructed, specifically including: Based on the operating data of each production unit under multiple preset operating conditions, the material flow income and expenditure, energy flow income and expenditure, and equipment loss of the entire plant system are modeled. The objective function for scheduling planning is initialized with the goal of maximizing the scheduling profit of the entire plant system. Based on the operating data of each production unit under multiple preset operating conditions, and combined with the production and consumption models of each production unit under multiple preset operating conditions, the material flow constraints, unit operation constraints, unit production and consumption constraints and unit cost constraints of the entire plant system are modeled, and multiple operation scheduling constraints of the objective function are set. Based on the objective function and the multiple operational scheduling constraints, a scheduling planning model for the entire plant system is constructed.
4. The production collaborative scheduling method based on a multi-level petrochemical plant structure according to claim 3, characterized in that, The material flow constraints include variable boundary constraints, inventory constraints, and non-inventory constraints; the device operation constraints include device processing capacity constraints, device single-mode processing capacity constraints, device processing capacity balance constraints, device mode quantity constraints, blending device constraints, and steam balance constraints.
5. The production collaborative scheduling method based on a multi-level petrochemical plant structure according to claim 3, characterized in that, The constraints on equipment production and consumption include equipment material production and consumption balance constraints, equipment single-mode material production and consumption balance constraints, equipment yield balance constraints, equipment mode change constraints, and equipment production and consumption model constraints.
6. The production collaborative scheduling method based on a multi-level petrochemical plant structure according to claim 1, characterized in that, Solving the scheduling planning model to obtain the scheduling plan for the entire plant system specifically includes: Based on a preset solver, the scheduling planning model is solved at a preset convergence accuracy to obtain the scheduling plan for the entire plant system; wherein, the scheduling plan includes the optimal processing volume of each production unit, the corresponding preset operating conditions, and the allocation of energy medium.
7. A production collaborative scheduling method based on a multi-level petrochemical plant structure according to any one of claims 1 to 6, characterized in that, The step of coordinating the production scheduling of the entire plant system according to the scheduling plan also includes: Real-time production data is collected to coordinate the production of the entire plant system according to the scheduling plan, and the production of the entire plant system is predicted based on the scheduling plan to obtain predicted production data. The real-time production data and the predicted production data are uploaded to the plant-wide system for visualization.
8. A production collaborative scheduling system based on a multi-level petrochemical plant structure, characterized in that, It includes a hierarchical data acquisition module, a production and consumption model construction module, a scheduling model construction module, and a model solving and scheduling module; The hierarchical data acquisition module is used to collect the operating data of each production unit under multiple preset operating conditions according to the hierarchical structure of the entire plant system to be scheduled. The production consumption model construction module is used to construct production consumption models for each production unit under multiple preset operating conditions based on the operating data of each production unit under multiple preset operating conditions; wherein, the production consumption model is used to characterize the relationship between the energy medium production consumption and the processing volume of the production unit under the corresponding preset operating conditions. The scheduling model construction module is used to construct a scheduling planning model for the entire plant system based on the operating data of each production unit under multiple preset operating conditions, with the optimization objective of maximizing the scheduling profit of the entire plant system; wherein, the scheduling planning model includes an objective function and multiple operational scheduling constraints; at least one operational scheduling constraint is constructed based on the production consumption model of each production unit under multiple preset operating conditions; The model solving and scheduling module is used to solve the scheduling planning model, obtain the scheduling plan for the entire plant system, and coordinate the production of the entire plant system according to the scheduling plan.
9. A production collaborative scheduling system based on a multi-level petrochemical plant structure according to claim 8, characterized in that, The production and consumption model construction module includes a first model construction unit, a second model construction unit, and a production and consumption model construction unit; The first model building unit is used to model the consumption of a variety of preset energy media based on the operating data of each production device under multiple preset operating conditions, so as to characterize the relationship between the energy media consumption and the processing volume of the production device, and to build the first production and consumption model of each production device for each energy medium under multiple preset operating conditions. The second model building unit is used to model the output of a variety of preset energy media based on the operating data of each production device under multiple preset operating conditions, so as to characterize the relationship between the output of energy media and the processing volume of the production device, and to build a second production and consumption model for each energy medium under multiple preset operating conditions for each production device. The production consumption model construction unit is used to obtain the production consumption model of each production device under multiple preset operating conditions based on the first and second production consumption models of each energy medium under multiple preset operating conditions.
10. A production collaborative scheduling system based on a multi-level petrochemical plant structure according to claim 8, characterized in that, The scheduling model construction module includes an objective function initialization unit, a constraint modeling setting unit, and a scheduling model construction unit; The objective function initialization unit is used to model the material flow income and expenditure, energy flow income and expenditure, and equipment loss of the entire plant system based on the operating data of each production unit under multiple preset operating conditions, and initialize the objective function for scheduling planning with the optimization objective of maximizing the scheduling profit of the entire plant system. The constraint modeling setting unit is used to model the material flow constraints, device operation constraints, device production and consumption constraints and device cost constraints of the whole plant system based on the operating data of each production device under multiple preset operating conditions and the production and consumption model of each production device under multiple preset operating conditions, and to set multiple operation scheduling constraints of the objective function. The scheduling model construction unit is used to construct the scheduling planning model of the entire plant system based on the objective function and the multiple operational scheduling constraints.