Multi-cycle collaborative production scheduling method of oil refining production and utility system under uncertain conditions

Abstract

The invention relates to a multi-cycle collaborative production scheduling method of an oil refining production and utility system under an uncertain condition. The method comprises the following steps: S1, obtaining operation data and price data of outsourcing power coal and electricity; s2, predicting the boundary of the uncertainty set according to the price data, and constructing a dynamic uncertainty set; s3, establishing a multi-cycle collaborative production scheduling model of a refinery plant production system and a public system under the dynamic uncertainty set; s4, introducing the model by adopting a distribution robust optimization method based on a dynamic uncertainty set; and S5, solving the multi-cycle collaborative production scheduling model of the oil refining production and utility system under the uncertain condition by adopting a robust dual reconstruction mode, and outputting a production plan. Compared with the prior art, the method has the advantages that the robustness and the conservative property of the production plan output by the model are improved, and the like.

Description

Technical Field

[0001] The present invention relates to, in particular to, a multi-period collaborative production scheduling method for oil refining production and public systems under uncertain conditions. Background Art

[0002] The core function of the refinery production system is to convert crude oil into various petroleum products, while the utility system provides the necessary electricity, steam and water resources to support the production process. The effective coordination of the two is an important basis for ensuring the efficient operation of the refinery. However, due to the existence of market uncertainties, such as fluctuations in the prices of purchased thermal coal and electricity, the nonlinear characteristics of equipment operation and cyclical changes in market demand, the coordinated optimization of refinery production and utility systems has become extremely complex. These uncertainties directly affect resource allocation, the rationality of production plans and overall production efficiency, thus having a significant impact on the economic benefits and environmental protection compliance of the refinery.

[0003] In recent years, with the rapid development of data-driven technologies such as machine learning, the application of predictive analysis methods based on historical data in the oil refining industry has become increasingly widespread. By analyzing and modeling the historical data of purchased thermal coal and electricity prices, the cyclical characteristics of price fluctuations can be effectively identified, and a dynamic uncertainty set can be constructed to provide a more accurate reference for optimizing decisions. However, existing methods generally ignore the dynamic characteristics of uncertain factors in a multi-period environment and cannot fully cope with the impact of cyclical fluctuations in raw material prices on production plans. At the same time, the recycling and utilization of waste heat resources in refineries has not been fully studied and optimized, resulting in waste of resources and reduced economic benefits. Summary of the invention

[0004] The purpose of the present invention is to provide a multi-period collaborative scheduling method for oil refining production and public systems under uncertain conditions in order to improve the robustness and conservatism of the production plan output by the model.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] A multi-period collaborative scheduling method for oil refining production and utility systems under uncertain conditions, the method comprising the following steps:

[0007] Step S1, obtaining the operation data of the entire refinery and the price data of purchased thermal coal and electricity in different periods;

[0008] Step S2: predicting the boundary of the uncertainty set based on the price data, and then constructing a dynamic uncertainty set;

[0009] Step S3: Under the dynamic uncertainty set, a multi-period collaborative scheduling model of the refinery production system and the utility system is established;

[0010] Step S4, using a distributed robust optimization method based on dynamic uncertainty sets to convert the model into a multi-period collaborative scheduling model for oil refining production and utility systems under uncertain conditions;

[0011] Step S5: Use a robust dual reconstruction method to solve the multi-period collaborative scheduling model of oil refining production and public systems under uncertain conditions, and output a production plan.

[0012] Furthermore, the dynamic uncertainty set is:

[0013]

[0014] Among them, ξ t,p represents the deviation of period t, is the deviation in the historical period, ρ i,p represents the regression coefficient, Π represents the length of the historical time period, represents a certain offset or fixed influence on the uncertainty at the current moment, U 1,t Represents a dynamic boundary.

[0015] Furthermore, the dynamic boundary is predicted based on the XGBoost model.

[0016] Furthermore, the objective function of the multi-period collaborative scheduling model of the refinery production system and the utility system of S3 is to maximize the economic benefit, and the economic benefit is:

[0017]

[0018] Among them, profit represents economic benefits, Price p,t Cost m,t Cost i,t Cost u,t 、Price outfuel,t ,Price e,t , and Respectively represent the purchase price of product p, the purchase cost of raw material m, the inventory cost of storage tank i, the operating cost of unit u, the price of purchased coal, the industrial electricity price, the treatment cost of CO2 and the treatment cost of SO2, V p,t 、V m,t 、Inv i,t 、V u,t 、V outfuel,b,t , V outE,t , and They represent the production of product p, the purchase quantity of crude oil m, the inventory level of storage tank i, the total flow of unit u, the amount of purchased coal, the amount of purchased electricity, the flow of CO2, and the flow of SO2.

[0019] Furthermore, the constraints of the multi-period collaborative scheduling model of the refinery production system and the utility system of S3 are the production planning model, the utility system model and the connection constraints of the two systems. The connection constraints of the two systems include fuel balance, steam balance and waste heat recovery steam balance. The fuel balance is specifically:

[0020] V infuel,b,t +V outfuel,b,t =V fuel,b,t b∈B,t∈T

[0021] Where V infuel,b,t Generate gas for production system, V outfuel,b,t For purchased coal, V fuel,b,t Represents the total fuel demand under boiler b.

[0022] Furthermore, the steam balance is specifically:

[0023]

[0024] Among them, et represents extraction steam turbine, ET represents extraction steam turbine set, BT represents back pressure steam turbine set, lv represents pressure reducing valve, LV represents pressure reducing valve set, u represents steam production unit, and U represents the set of processing devices in production system and utility system;

[0025] represents the amount of high-pressure steam flowing out of the extraction turbine during period t, V hs,lv,t Indicates the high-pressure steam flow rate from the pressure reducing valve lv, V hs,t represents the total flow rate of high-pressure steam in the system, Indicates the amount of high-pressure steam flowing into the downstream turbine, DS hs,u,t Represents the amount of high-pressure steam required by processing unit u in the production system; V ms,lv,t Indicates the medium pressure steam flow rate from the pressure reducing valve lv, represents the amount of medium-pressure steam flowing out of the extraction turbine during period t, V ms,lv,t Indicates the medium pressure steam flow rate from the pressure reducing valve lv, V ms,t Indicates the total flow rate of medium-pressure steam in the system, Indicates the amount of medium-pressure steam flowing into the downstream turbine, DS ms,u,t It represents the amount of medium-pressure steam required by process unit u in the production system; represents the amount of low-pressure steam flowing out of the extraction steam turbine et in period t, V ls,lv,t Indicates the low-pressure steam flow rate from the pressure reducing valve lv, V ls,t Indicates the total flow of low-pressure steam in the system, DS ls,u,t Represents the amount of low-pressure steam required by processing unit u in the production system.

[0026] Furthermore, the waste heat recovery steam balance is:

[0027]

[0028]

[0029] Where: the total amount of steam generated by the steam production unit u at the steam level mls in period t The production flow rate of the unit V u,t , coefficient C psu,mls , PSU represents the set of self-generated steam units, MLS represents the set of steam levels, and steam demand DS mls,u,t , steam demand DS of non-steam producing units at steam level mls mls,u,t Externally produced steam supply and the steam consumed by the unit V u,mls,t The sum of the steam demand DS for the steam production unit mls,u,t is the unit's own steam usage External self-generated steam supply and the steam consumed in the unit V u,mls,t The sum of They respectively represent the total amount of self-produced high-pressure steam, the total amount of self-produced medium-pressure steam and the total amount of self-produced low-pressure steam.

[0030] Furthermore, the objective function of the multi-period collaborative scheduling model of oil refining production and utility systems under uncertain conditions is:

[0031]

[0032] Among them, c T x t represents direct cost, x t represents the one-stage decision variable of period t, and c represents the decision variable x t The relevant cost factor, It represents the expected operation, that is, the expected value calculation of the function under uncertainty conditions. represents the probability distribution of uncertainty parameters, D represents the probability distribution set, and f represents the probability distribution of decision variables x. t and the uncertainty parameter ξ t The relevant objective function, ξ t represents the uncertainty parameter, which is a vector describing some uncertain factors in the system under the time period t, b represents the constant vector on the right side of the constraint, W represents the constraint matrix, and defines the auxiliary variable y t The constraints that are satisfied, y t represents the two-stage decision variable adjusted according to the uncertainty condition, and h represents the decision variable x tRelated constraint functions, Y represents the set of auxiliary decision variables, that is, all possible y t is a set of, P is a matrix, its role is to transform the uncertainty parameter ξ t and the decision variable y t Combine and weight them to affect the value of the objective function.

[0033] Furthermore, the specific steps of solving the multi-period collaborative scheduling model of oil refining production and utility systems under uncertain conditions by using the robust dual reconstruction method are as follows:

[0034] The multi-period collaborative scheduling model of oil refining production and utility systems under uncertain conditions is transformed into a robust dual model and then solved. The robust dual model is:

[0035]

[0036]

[0037] Among them, λ t represents the uncertainty budget variable in period t, controlling the robustness level of the model to uncertainty, s n,t Represents auxiliary variables, y n,t represents the two-stage decision variable for scenario n in period t, used for adjustment plans under specific circumstances, γ n,t The dual variable that represents the upper limit of the control uncertainty deviation, η n,t The dual variable θ represents the lower limit of the control uncertainty deviation. t represents the weight coefficient of the robustness level, N represents the number of uncertainty scenarios, n represents the index of the uncertainty scenario, q represents the weight vector, represents the actual uncertainty parameter under scenario n with period t, represents the upper bound of the uncertainty set, Denotes the lower bound of the uncertainty set.

[0038] Furthermore, the operation data and price data of thermal coal and electricity purchased from outside at different periods include process parameters of the production unit, material balance data, energy consumption indicators and price data of thermal coal and electricity purchased from outside at different periods.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] The present invention integrates the refinery common system model into the multi-period production planning model, and takes into account the waste heat recovery of the refinery production equipment, thereby achieving cost reduction and improved production efficiency. At the same time, the data-driven dynamic uncertainty set method is applied to the multi-period collaborative scheduling model, which can more accurately reflect the price fluctuations in the actual market and improve the robustness and flexibility of the production plan. The introduction of uncertainty sets enables the solution model to show significant advantages in optimizing solution quality, model efficiency and economic benefits, and can achieve a dynamic trade-off between robustness and conservatism by adjusting the Wasserstein radius. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a step diagram of the multi-period collaborative scheduling method of oil refining production and public system under uncertain conditions of the present invention;

[0042] Figure 2 To simplify the refinery production system flow chart.

[0043] Figure 3 To simplify the refinery utility system flow chart.

[0044] Figure 4 Optimize flow charts for integration of refinery production systems and utility systems. DETAILED DESCRIPTION

[0045] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0046] The present invention proposes a multi-period collaborative scheduling method for oil refining production and public systems under uncertain conditions, aiming to cope with the impact of uncertain factors such as raw material price fluctuations on the operating efficiency of oil refineries in complex market environments. The method takes multi-period optimization as the core, comprehensively considers the periodic fluctuation characteristics of thermal coal and electricity prices, accurately identifies the dynamic range of price fluctuations by introducing a prediction model based on XGBoost, and constructs a dynamic uncertainty set to provide a scientific basis for the collaborative optimization of oil refining and public systems. The invention is based on a data-driven dynamic optimization framework, combined with the operating data and historical price data of the refinery, to achieve accurate prediction and dynamic adjustment of resource supply and demand. At the same time, waste heat recovery technology is integrated into the optimization model to effectively integrate the waste heat resources generated in the oil refining process, improve the utilization efficiency of high, medium and low pressure steam, and reduce energy consumption. By constructing a multi-period optimization model with strong robustness and high adaptability, the present invention meets strict environmental protection regulations while improving production economic benefits, and provides technical support for the transformation, upgrading and sustainable development of the oil refining industry.

[0047] The method of the present invention comprises the following steps, and the flow chart is as follows: Figure 1 As shown:

[0048] Step S1, obtaining the operation data of the entire refinery and the price data of purchased thermal coal and electricity in different periods;

[0049] Step S2: predicting the boundary of the uncertainty set based on the price data, and then constructing a dynamic uncertainty set;

[0050] Step S3: Under the dynamic uncertainty set, a multi-period collaborative scheduling model of the refinery production system and the utility system is established;

[0051] Step S4, using a distributed robust optimization method based on dynamic uncertainty sets to convert the model into a multi-period collaborative scheduling model for oil refining production and utility systems under uncertain conditions;

[0052] Step S5: Use a robust dual reconstruction method to solve the multi-period collaborative scheduling model of oil refining production and public systems under uncertain conditions, and output a production plan.

[0053] Figure 2 A simplified refinery production system flow chart according to the present invention is shown in FIG. Figure 2 As shown, in the refinery process in this embodiment, the main raw materials of the refinery include six kinds of crude oil (SAM, OMN, SHL, IRL, SAX, CBA) and purchased wax oil (BVG), purchased heavy aromatics (BHR), purchased methanol (BME), purchased hydrogen (BH2), purchased reforming raw material (BRF), purchased MTBE (MTB), and purchased ethylbenzene (BEB).

[0054] The processing units include two sets of atmospheric and vacuum units (CDU1, CDU2), one set of light hydrocarbon recovery unit (SCUT), one set of reforming pre-hydrogenation unit (SR1P), two sets of catalytic cracking units (FCC1, FCC2), one set of slurry top-pulling unit (OPOH), one set of high-pressure hydrocracking unit (SHCU), one set of delayed coking unit (DCFC), one set of solvent deasphalting unit (SDFC), one set of S-ZORB unit (S-ZORB), two sets of gas separation units (GS1, GS2), one set of polypropylene unit (SPPU), one set of MTBE unit, one set of PSA unit, one set of ethylbenzene unit (SEBN), one set of styrene unit (SSTR), one set of alkylation unit (SALK), one set of benzene extraction combined unit (CBEU), and 7 sets of hydrogenation units (HF1-HF8), a total of 27 units.

[0055] The storage tanks include six types of crude oil storage tanks, gasoline storage tanks, aviation kerosene storage tanks, diesel storage tanks, ethylene naphtha storage tanks, residual oil storage tanks, etc.

[0056] A total of 16 final products are produced, namely No. 92 gasoline (W92), No. 95 gasoline (W95), jet fuel (JET), vinyl naphtha (L10), xylene (EEN), motor diesel (XYL), road asphalt (A60), commercial dry gas (PGS), polypropylene (PPP), petroleum coke (CCK), sulfur (SUL), styrene (STR), benzene (BNZ), commercial liquefied gas (PLG), trimeryl (PUL) and liquid ammonia (NH3).

[0057] A total of 13 kinds of raw materials were purchased, namely:

[0058] Six types of crude oil (SAM, OMN, SHL, IRL, SAX, CBA) as well as purchased wax oil (BVG), purchased heavy aromatics (BHR), purchased methanol (BME), purchased hydrogen (BH2), purchased reforming raw material (BRF), purchased MTBE (MTB), and purchased ethylbenzene (BEB).

[0059] Figure 3 The simplified refinery utility system flow chart of the present invention is as follows: Figure 3 As shown, in the refinery process of this embodiment, 3 sets of boilers are responsible for providing steam for production. The boilers use fuel oil, natural gas or coal as fuel to ensure a stable supply of heat sources; 4 sets of extraction steam turbines meet different pressure requirements by extracting part of the steam, and provide power to support the operation of key equipment; 15 sets of back-pressure steam turbines use the low-pressure steam generated by high-pressure steam in the production process to provide energy for low-pressure steam-using equipment and improve the energy efficiency of the system; 3 sets of pressure reducing valves are used to adjust the steam pressure to meet the specific requirements of different production units for steam pressure. These public facilities together constitute the core part of the energy and power system of the refinery, ensuring efficient energy utilization and stable operation in the production process of the refinery.

[0060] The units that are considered to be able to produce their own steam include two atmospheric and vacuum units (CDU1, CDU2), two catalytic cracking units (FCC1, FCC2), a reforming pre-hydrogenation unit (SR1P) and a delayed coking unit (DCFC).

[0061] Figure 4 Optimize flow charts for integration of refinery production systems and utility systems, such as Figure 4 As shown in the figure, the production system includes atmospheric and vacuum distillation units, secondary processing units and blending units, and finally outputs finished products. The utility system consists of boilers and steam turbines to provide energy support for the production system.

[0062] For the production system, the atmospheric and vacuum distillation unit: processes crude oil and separates light and heavy components; the secondary processing unit: further processes heavy components to improve product quality; the blending unit: blends the processed components into final products that meet standards. For the public system, the boiler: produces steam to supply the production system and steam turbine; the steam turbine: uses steam to generate electricity or drive equipment, and outputs waste steam for different stages of the production system. The production system not only consumes energy from the public system to produce gasoline and diesel, but also produces some fuel to provide fuel resources for the public system.

[0063] At the same time, the utility system must provide stable steam and electricity to provide operating power and a suitable production environment for the refinery's production units.

[0064] The steps of the present invention are specifically:

[0065] Step S1: Collect the operation data of the entire refinery and the price data of purchased thermal coal and electricity in different periods.

[0066] First, obtain the refinery's operating data, including process parameters of production units, material balance data, energy consumption indicators, etc., through real-time monitoring equipment, data acquisition systems or historical records; second, collect price data of purchased thermal coal and electricity in different periods from the market or supply chain management system; finally, combine data cleaning and preprocessing technology to ensure the integrity, accuracy and consistency of the collected data.

[0067] Step S2: Predict the boundary of the uncertainty set based on the price history data, and then construct a dynamic uncertainty set.

[0068] Dynamic Uncertain Set To describe the periodic fluctuation of coal prices, this uncertainty set has a time-varying boundary. The traditional static uncertainty set A fixed range of fluctuations is defined, while the dynamic uncertainty set updates the bounds in real time by combining time series data and historical deviation estimates. This can more accurately reflect the actual fluctuations. The dynamic uncertainty set is defined as follows:

[0069]

[0070] Among them, ξ t,p represents the deviation at time t, is the deviation in the historical period, ρ i,p represents the regression coefficient, and the dynamic boundary U 1,t It is predicted by the XGBoost model.

[0071] The XGBoost model predicts uncertainty boundaries for future periods by continuously updating historical data. The model is trained using a supervised learning algorithm with the deviation range of coal price fluctuations as the target variable. XGBoost uses gradient boosted decision trees to learn and predict uncertainty boundaries, thereby generating uncertainty set boundaries for a given time period. The following pseudocode shows how the model estimates the deviation with new input data at the end of each period: Algorithm: XGBoost predicts dynamic uncertainty sets

[0072] 1. Input:

[0073] I: Instance set of the current node (year, period).

[0074] m: Deviation dimension.

[0075] 2. Initialization:

[0076] Calculate the gain: gain←0.

[0077] Define global parameters: G←∑ i g i ,H←∑ i h i .

[0078] 3. Loop through nodes: For k=1 to m, perform the following operations:

[0079] Initialize local parameters: G L ←0,H L ←0.

[0080] According to the feature x k , sorting the instances by their values.

[0081] 4. Feature splitting calculation: traverse the sorted instance j:

[0082] Update the accumulated value of the left child node: G L ←G L +g j , H L ←H L +h j .

[0083] Update the accumulated value of the right child node: G R ←GG L ,H R ←HH L .

[0084] Calculate the split score:

[0085] 5. Output:

[0086] The bias forecast of the uncertainty set for the next period.

[0087] Step S3: Under the condition of uncertainty in the prices of purchased coal and electricity, a multi-period collaborative scheduling model for the refinery production system and the public system is established.

[0088] The present invention systematically optimizes the synergistic relationship between the refinery production system and the utility system by integrating the refinery utility system model into the multi-period production planning model. The refinery production system and the utility system each have different functions - the production system is responsible for crude oil processing and product output, while the utility system provides necessary support such as energy and steam. However, the two do not operate independently, but are highly coupled through energy and material flows. Therefore, it is particularly necessary to integrate and optimize the two in the model, which can not only improve overall energy efficiency, but also significantly reduce energy consumption and costs.

[0089] In addition, as a key link in optimizing synergy, waste heat recovery is necessary in terms of reducing energy waste, improving energy efficiency and protecting the environment. Refinery production equipment releases a large amount of heat energy during the processing process. If it is discharged directly, it will not only cause waste of resources, but also may cause thermal pollution. Through the waste heat recovery system, this part of the heat energy can be efficiently utilized, for example, for boiler water preheating or low-pressure steam production, thereby reducing dependence on external energy sources such as coal and electricity. This not only helps to achieve the low-carbon goal of refinery operation, but also improves production efficiency while reducing costs. Therefore, the present invention combines waste heat recovery with multi-cycle optimization, which not only improves the completeness of the model in theory, but also achieves the win-win goal of efficient energy utilization and cost minimization in practice.

[0090] In the production system, each processing unit requires steam and electricity to support operation. These energies are provided by the utility system. At the same time, the gas produced by the production system can be used as fuel for the boiler.

[0091] Fuel balance between production system and utility system. The boiler fuel source is partly from the gas generated by the production system and partly from purchased coal. The functional expression is:

[0092] V infuel,b,t +V outfuel,b,t =V fuel,b,t b∈B,t∈T

[0093] Where V infuel,b,t Generate gas for production system, V outfuel,b,t To purchase coal from outside.

[0094] Steam balance between production system and utility system. Three levels of steam are considered, including high pressure, medium pressure and low pressure steam. The generation and consumption of high, medium and low pressure steam are key energy balance factors, and the functional expression is:

[0095]

[0096] In the formula, the two terms V hs,t They are the generation and consumption of high-pressure steam. Specifically, they include the amount of steam provided by the high-pressure steam generating equipment (such as boilers or thermal equipment) in the refinery and the amount of high-pressure steam consumed for driving equipment, thermal demand or allocation to the next level of pressure (such as decompression process); the two V ms,t The generation and consumption of medium-pressure steam include the decompression of high-pressure steam or the output of medium-pressure steam generation equipment, as well as the direct application of medium-pressure steam in process equipment or the continued decompression to low pressure; ls,t They are the generation and consumption of low-pressure steam, specifically including the output of medium-pressure steam reduction or low-pressure steam generation equipment and the demand for low-pressure steam for process heating, pipeline insulation or other low-pressure application scenarios.

[0097] In the process of coordinated optimization of oil refining production and utility systems, the waste heat recovery system is integrated into the steam network to further reduce dependence on the utility system and consumption of raw materials. The functional expression is:

[0098]

[0099] Where, (1) represents the total steam generated by the steam production unit u at steam level mls in period t is obtained by converting the unit production flow V u,t Multiply by coefficient C psu,mls The latter represents the steam output per unit of production flow. (2) represents the steam demand DS at steam level mls for non-steam producing units (i.e. units that do not generate steam themselves). mls,u,t Externally produced steam supply and the steam consumed by the unit V u,mls,t The sum of ; (3) shows that for the steam production unit, the steam demand DS mls,u,t is the unit's own steam usage External self-generated steam supply and the steam consumed in the unit V u,mls,tThe total amount of high-pressure steam generated by all steam production units (excluding their own steam consumption) must be equal to the total amount of external high-pressure steam supplied to other units; Formula (5) indicates that the total amount of medium-pressure steam generated by all steam production units, minus their own consumption, plus the medium-pressure steam obtained by heat exchange with high-pressure steam, must be equal to the total amount of medium-pressure steam provided to all units as external steam; Formula (6) indicates that the total amount of low-pressure steam generated by all steam production units, minus their own consumption, plus the low-pressure steam obtained by heat exchange with medium-pressure steam, must be equal to the total amount of low-pressure steam provided to all units as external steam.

[0100] The objective function is to maximize economic benefits;

[0101] The constraints are the production planning model, the common system model and the connection constraints between the two systems.

[0102] The corresponding constraints of the production planning model include supply and demand constraints, processing equipment constraints, blending constraints and storage tank conditions;

[0103] The corresponding constraints of the utility system model include boiler model, steam turbine model, pressure reducing valve model and power constraints;

[0104] The corresponding constraints of the connection constraints between the two systems include fuel balance, steam balance and waste heat recovery steam balance.

[0105] For the multi-period production planning model, since the refinery utility system model is introduced, the utility system energy cost also needs to be included in the objective function. The expression corresponding to the economic benefit model is:

[0106]

[0107] Among them, in the objective function, Price p,t Cost m,t Cost i,t Cost u,t 、Price outfuel,t ,Price e,t , and They represent the purchase price of product p, the purchase cost of raw material m, the inventory cost of storage tank i, the operating cost of unit u, the price of purchased coal, the industrial electricity price, the treatment cost of CO2, and the treatment cost of SO2. p,t 、V m,t 、Inv i,t 、V u,t 、V outfuel,b,t , V outE,t , and They represent the production of product p, the purchase quantity of crude oil m, the inventory level of storage tank i, the total flow of unit u, the amount of purchased coal, the amount of purchased electricity, the flow of CO2, and the flow of SO2.

[0108] The multi-period collaborative scheduling model includes a multi-period refinery production planning model and a refinery utility system model;

[0109] The production system and public system of an oil refinery are closely related. Fluctuations in thermal coal and electricity prices will affect the cost of the public system, and then affect the energy consumption and by-product utilization of the production system, and have a significant impact on production plans and economic benefits. Therefore, studying the optimization of collaborative systems under uncertain conditions of coal and electricity prices is crucial to improving the energy efficiency and economic benefits of oil refineries.

[0110] Step S4, apply the distributed robust optimization method based on dynamic uncertain sets to the current multi-period collaborative scheduling model to adapt to the cyclical fluctuations and random changes of market prices, and rewrite the objective function into the following form:

[0111]

[0112] The main goal of the model is to minimize the cost while taking into account the uncertainty of the future state ξ t Among them, c T x t represents direct cost, and It summarizes the expected losses due to uncertainty.

[0113] At the same time, using dynamic uncertain set To describe the periodic fluctuation of coal prices, this uncertainty set has a time-varying boundary. The traditional static uncertainty set A fixed range of fluctuations is defined, while the dynamic uncertainty set updates the bounds in real time by combining time series data and historical deviation estimates. This can more accurately reflect the actual fluctuations. The dynamic uncertainty set is defined as follows:

[0114]

[0115] Among them, ξ t,p represents the deviation at time t, is the deviation in the historical period, ρ i,p represents the regression coefficient, and the dynamic boundary U 1,t It is predicted by the XGBoost model.

[0116] Step S5, using a robust dual reconstruction method to solve the multi-period collaborative scheduling model of oil refining production and public systems under uncertain conditions as described in claim 8, the function expression is:

[0117]

[0118]

[0119] The robust dual model integrates all variables and constraints, ensuring comprehensive management of uncertainties and achieving overall optimization by balancing the needs of different stakeholders.

[0120] In the previous collaborative model of the refinery production system and the public system, the system operation focused on meeting production and energy needs, but did not fully consider the role of waste heat recovery, resulting in difficulty in further improving energy efficiency. The multi-cycle integrated model proposed in the present invention achieves a closer coupling and efficient operation between the two by incorporating the waste heat recovery system into the collaborative optimization of the production and public systems. Specifically, a large amount of waste heat generated during the operation of the processing unit can be converted into low-pressure or medium-pressure steam through the waste heat recovery system and directly supplied to other units for use, thereby reducing dependence on the public system and reducing the operating burden of the public system. The core of this collaborative model is to make full use of the waste heat generated by the production unit, realize the recycling of internal energy, and dynamically optimize the energy distribution between the production system and the public system. Through the introduction of waste heat recovery, the model can significantly reduce the consumption of external energy such as coal and electricity, and improve the overall economic benefits and environmental friendliness of the system.

[0121] To verify this, a comparative experiment was conducted in two oil refining industry cases to determine whether to allow excess heat to be recovered from the oil refining process for steam production. The results are shown in Table 1. Case 1 includes a self-generated steam module in the model, allowing excess heat to be recovered from the oil refining process for steam production, and does not consider the actual fluctuations in coal and electricity prices; in Case 2, this steam recovery module is not considered, and the actual fluctuations in coal and electricity prices are not considered.

[0122] From the experimental results, the economic benefits of Case 1 are always higher than those of Case 2, regardless of whether the model is running under single-cycle or multi-cycle conditions. This shows that considering self-generated steam in the model can significantly improve economic performance. Specifically, although Case 1 involves more continuous variables and constraints than Case 2, the increase in complexity does not significantly affect the computational efficiency. For example, the solution time of Case 1 only slightly increases in all cycles. The percentage difference between the two cases is always low, indicating that the two models perform well in terms of solution accuracy. In addition, the target value representing the economic benefits increases significantly in Case 1 with the increase of planning cycles. For example, in the 48-cycle scenario, the economic benefits of Case 1 reach 1.385618e+10, while that of Case 2 is 1.309030e+10, and the gap between the two is significant. As the number of cycles increases, the economic benefits of Case 1 continue to grow, and the target value increases by 0.13182e+08, 0.51948e+08, and 1.05158e+09 in 12, 36, and 48 cycles, respectively. This suggests that the introduction of a self-generated steam recovery module can improve the overall system efficiency and economic benefits, especially in long-term operation.

[0123] Table 1 Experimental results of different production planning model methods under two cases

[0124]

[0125] Various methods for dealing with uncertainty have been widely used, with robust optimization and stochastic programming being two of the most commonly used approaches. While the effectiveness of robust optimization has been demonstrated in various fields, it is noteworthy that these achievements have been achieved without relying on probabilistic information. In reality, the factors that lead to uncertainty often follow a certain probability distribution. This observation highlights the potential limitations of robust optimization and motivates people to further explore methods that integrate probabilistic information to obtain more comprehensive uncertainty management methods.

[0126] Dynamic uncertainty sets can adjust the uncertainty description in real time according to the periodic fluctuations of the environment (such as periodic changes in coal prices and electricity prices), so that the optimization model can dynamically adapt to changes in the external environment. This flexibility avoids the problem that traditional static uncertainty sets may be too conservative or inaccurate, thereby improving the practicality and accuracy of production scheduling. Conventional methods usually use static uncertainty sets to model the uncertainty of the external environment, but they cannot reflect periodic fluctuations (such as coal prices and electricity prices) and dynamic changes, which may lead to overly conservative or inflexible results. Dynamic uncertainty sets can adjust the uncertainty range according to historical data and real-time environmental changes, more accurately describe complex uncertainties, and improve the prediction and adaptability of the model.

[0127] The analysis principle of the model in S5 is:

[0128] To do this, rewrite the objective function as follows:

[0129]

[0130] The main goal of the model is to minimize the cost while taking into account the uncertainty of the future state ξ t Among them, c T x t represents direct cost, and It summarizes the expected losses due to uncertainty.

[0131] By introducing the Lagrange multiplier λ t , Formula (8) reformulates the expectation part of the objective function as a minimization problem. This transformation not only facilitates the handling of uncertainty, but also makes it possible to balance the relationship between the expected loss and the objective function during the optimization process.

[0132]

[0133] Here, a reasonable choice of λ t It can help decision makers deal with relevant trade-offs, thereby enhancing the robustness and reliability of the optimization process.

[0134] By introducing the slack variable s n,t , the above framework is further constructed as follows:

[0135]

[0136] Among them, the slack variable s n,t For modeling each sample point This enhances the flexibility of the optimization process and enables it to adapt to a variety of scenarios represented by the sample space.

[0137] Based on the duality principle, the dual relationship between maximization and minimization is demonstrated. This step simplifies the processing of the original problem and enables the optimization process to utilize the known optimal solution characteristics.

[0138]

[0139] Next, further constraints are introduced into the optimization model:

[0140]

[0141] Here, formula (12) ensures that the maximum expected loss does not exceed the slack variable s n,t At the same time, it also imposes constraints on the Lagrange multipliers |z n,t | * ≤λ t, thus limiting the impact of the multiplier on the overall optimization.

[0142] By introducing the auxiliary variable y n,t ,γ n,t ,η n,t , the formula is further constructed as follows:

[0143]

[0144]

[0145] By integrating the results of all the previous formulas, an optimization model containing all variables and constraints is finally formed:

[0146]

[0147] The robust dual model integrates all variables and constraints, ensuring comprehensive management of uncertainties and achieving overall optimization by balancing the needs of different stakeholders.

[0148] The present invention takes into account the uncertainty caused by the fluctuation of coal and electricity prices, and evaluates the performance of different optimization methods in dealing with the uncertainty in the oil refining production system. The experiment includes Case 1 and Case 3. In Case 1, Case 1 includes a self-generated steam module in the model, allowing excess heat to be recovered from the oil refining process for steam production, and does not consider the actual fluctuations of coal prices and electricity prices; in Case 3, a self-generated steam module is included, and the actual fluctuations of coal prices and electricity prices are considered. Four different solutions are applied and compared to these two cases: deterministic methods, robust optimization methods, distributed robust optimization methods, and dynamic distributed robust optimization methods based on dynamic uncertainty sets. The experimental results are shown in Table 2. Compared with the performance of other uncertain methods, the dynamic distributed robust optimization method based on dynamic uncertainty sets has the best solution result. Since the dynamic uncertainty set predicts the boundary according to historical data, the robustness of the model is increased, so the economic benefit value is improved. Compared with the distributionally robust optimization method, the dynamic distributionally robust optimization method based on dynamic uncertainty sets adds corresponding auxiliary variables in each cycle. The auxiliary variables can decompose complex constraints and reduce the complexity of the model, which is more conducive to solving the model. Therefore, the solution efficiency is improved to a certain extent.

[0149] Table 2 Comparison of the effects of different optimization methods

[0150]

[0151]

[0152] The present invention proposes a multi-period collaborative scheduling method for refinery production and public systems under uncertain conditions. Under the premise of considering the uncertainty of market thermal coal and electricity prices, the refinery public system model is integrated into the multi-period production planning model to realize the adaptive maintenance of the constant pressure reduction unit. At the same time, the data-driven dynamic uncertainty method is applied to the current multi-period model to more accurately reflect the price fluctuations in the actual market. Finally, the dynamic uncertainty set is introduced on the basis of the existing distributed robust optimization model, and a new solution model is proposed to improve the robustness and flexibility of the existing production plan and obtain the optimal production plan for the refinery production plan.

[0153] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A multi-period collaborative scheduling method for oil refining production and utility systems under uncertain conditions, characterized in that: The method comprises the following steps: Step S1, obtaining the operation data of the entire refinery and the price data of purchased thermal coal and electricity in different periods; Step S2: predicting the boundary of the uncertainty set based on the price data, and then constructing a dynamic uncertainty set; Step S3: Under the dynamic uncertainty set, a multi-period collaborative scheduling model of the refinery production system and the utility system is established; Step S4, using a distributed robust optimization method based on dynamic uncertainty sets to convert the model into a multi-period collaborative scheduling model for oil refining production and utility systems under uncertain conditions; Step S5: Use a robust dual reconstruction method to solve the multi-period collaborative scheduling model of oil refining production and public systems under uncertain conditions, and output a production plan.

2. The multi-period collaborative scheduling method for oil refining production and utility systems under uncertain conditions according to claim 1, characterized in that: The dynamic uncertainty set is: Among them, ξ t,p represents the deviation of period t, is the deviation in the historical period, ρ i,p represents the regression coefficient, Π represents the length of the historical time period, represents a certain offset or fixed influence on the uncertainty at the current moment, U 1,t Represents a dynamic boundary.

3. The multi-period collaborative scheduling method for oil refining production and utility systems under uncertain conditions according to claim 2, characterized in that: The dynamic boundary is predicted based on the XGBoost model.

4. The multi-period collaborative scheduling method for oil refining production and utility systems under uncertain conditions according to claim 2, characterized in that: The objective function of the multi-period collaborative scheduling model of the refinery production system and the utility system of S3 is to maximize the economic benefit, which is: Among them, profit represents economic benefits, Price p,t Cost m,t Cost i,t Cost u,t 、Price outfuel,t , Price e,t , and Respectively represent the purchase price of product p, the purchase cost of raw material m, the inventory cost of storage tank i, the operating cost of unit u, the price of purchased coal, the industrial electricity price, the treatment cost of CO2 and the treatment cost of SO2, V p,t 、V m,t 、Inv i,t 、V u,t 、V outfuel,b,t , V outE,t , and They represent the production of product p, the purchase quantity of crude oil m, the inventory level of storage tank i, the total flow of unit u, the amount of purchased coal, the amount of purchased electricity, the flow of CO2, and the flow of SO2.

5. The multi-period collaborative scheduling method for oil refining production and utility systems under uncertain conditions according to claim 4, characterized in that: The constraints of the multi-period collaborative scheduling model of the refinery production system and the utility system of S3 are the production planning model, the utility system model and the connection constraints of the two systems. The connection constraints of the two systems include fuel balance, steam balance and waste heat recovery steam balance. The fuel balance is specifically: V infuel,b,t +V outfuel,b,t =V fuel,b,t b∈B,t∈T Where V infuel,b,t Generate gas for production system, V outfuel,b,t For purchased coal, V fuel,b,t Represents the total fuel demand under boiler b.

6. The multi-period collaborative scheduling method for oil refining production and utility systems under uncertain conditions according to claim 5, characterized in that: The steam balance is as follows: V ls,t =Σ u∈U DS ls,u,t t∈T Among them, et represents extraction steam turbine, ET represents extraction steam turbine set, BT represents back pressure steam turbine set, lv represents pressure reducing valve, LV represents pressure reducing valve set, u represents steam production unit, and U represents the set of processing devices in production system and utility system; represents the amount of high-pressure steam flowing out of the extraction turbine during period t, V hs,lv,t Indicates the high-pressure steam flow rate from the pressure reducing valve lv, V hs,t represents the total flow rate of high-pressure steam in the system, Indicates the amount of high-pressure steam flowing into the downstream turbine, DS hs,u,t Represents the amount of high-pressure steam required by processing unit u in the production system; V ms,lv,t Indicates the medium pressure steam flow rate from the pressure reducing valve lv, represents the amount of medium-pressure steam flowing out of the extraction turbine during period t, V ms,lv,t Indicates the medium pressure steam flow rate from the pressure reducing valve lv, V ms,t Indicates the total flow rate of medium-pressure steam in the system, Indicates the amount of medium-pressure steam flowing into the downstream turbine, DS ms,u,t represents the amount of medium-pressure steam required by process unit y in the production system; represents the amount of low-pressure steam flowing out of the extraction steam turbine et in period t, V ls,lv,t Indicates the low-pressure steam flow rate from the pressure reducing valve lv, V ls,t Indicates the total flow of low-pressure steam in the system, DS ls,u,t Represents the amount of low-pressure steam required by processing unit u in the production system.

7. The multi-period collaborative scheduling method for oil refining production and utility systems under uncertain conditions according to claim 6, characterized in that: The waste heat recovery steam balance is: Where: the total amount of steam generated by the steam production unit u at the steam level mls in period t The production flow rate of the unit V u,t , coefficient C psu,mls , PSU represents the set of self-generated steam units, MLS represents the set of steam levels, and steam demand DS mls,u,t , steam demand DS of non-steam producing units at steam level mls mls,u,t Externally produced steam supply and the unit consumed steam V u,mls,t The sum of the steam demand DS for the steam production unit mls,u,t is the unit's own steam usage External self-generated steam supply and the steam consumed in the unit V u,mls,t The sum of They respectively represent the total amount of self-produced high-pressure steam, the total amount of self-produced medium-pressure steam and the total amount of self-produced low-pressure steam.

8. The multi-period collaborative scheduling method for oil refining production and utility systems under uncertain conditions according to claim 3, characterized in that: The objective function of the multi-period collaborative scheduling model of oil refining production and utility systems under uncertain conditions is: Among them, c T x t represents direct cost, x t represents the one-stage decision variable of period t, and c represents the decision variable x t The relevant cost factor, It represents the expected operation, that is, the expected value calculation of the function under uncertainty conditions. represents the probability distribution of uncertainty parameters, D represents the probability distribution set, and f represents the probability distribution of decision variables x. t and the uncertainty parameter ξ t The relevant objective function, ξ t represents the uncertainty parameter, which is a vector describing some uncertain factors in the system under the time period t, b represents the constant vector on the right side of the constraint, W represents the constraint matrix, and defines the auxiliary variable y t The constraints that are satisfied, y t represents the two-stage decision variable adjusted according to the uncertainty condition, and h represents the decision variable x t Related constraint functions, Y represents the set of auxiliary decision variables, that is, all possible y t is a set of, P is a matrix, its role is to transform the uncertainty parameter ξ t and the decision variable y t Combine and weight them to affect the value of the objective function.

9. The multi-period collaborative scheduling method for oil refining production and utility systems under uncertain conditions according to claim 8, characterized in that: The specific steps of solving the multi-period collaborative scheduling model of oil refining production and utility systems under uncertain conditions by using the robust dual reconstruction method are as follows: The multi-period collaborative scheduling model of oil refining production and utility systems under uncertain conditions is transformed into a robust dual model and then solved. The robust dual model is: s.t.Ax t ≤b ||(P) T y n,t -c n,t +n n,t || * ≤λ t c n,t ≥0,η n,t ≥0 Wy n,t ≥h(x t ) Among them, λ t represents the uncertainty budget variable in period t, controlling the robustness level of the model to uncertainty, s n,t Represents auxiliary variables, y n,t represents the two-stage decision variable for scenario n in period t, used for adjustment plans under specific circumstances, γ n,t The dual variable that represents the upper limit of the control uncertainty deviation, η n,t The dual variable θ represents the lower limit of the control uncertainty deviation. t represents the weight coefficient of the robustness level, N represents the number of uncertainty scenarios, n represents the index of the uncertainty scenario, q represents the weight vector, represents the actual uncertainty parameter under scenario n with period t, represents the upper bound of the uncertainty set, ξ t Denotes the lower bound of the uncertainty set.

10. The multi-period collaborative scheduling method for oil refining production and utility systems under uncertain conditions according to claim 1, characterized in that: The operation data and price data of thermal coal and electricity purchased from outside at different periods include process parameters of the production unit, material balance data, energy consumption indicators and price data of thermal coal and electricity purchased from outside at different periods.

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