Multi-cycle optimization method and device of energy and mass coupling system and storage medium

Abstract

The invention relates to a multi-cycle optimization method and device of an energy and mass coupling system and a storage medium. The method comprises the following steps: constructing a component model of a catalytic cracking process according to related parameters of each device in a catalytic cracking device group; constructing a component model of the steam system according to the related parameters of each device in the steam system, and determining required constraint conditions; according to the component model of the catalytic cracking process, the component model of the steam system and the required constraint conditions, an objective function of the energy mass coupling system is established, and the objective function aims at minimizing the total operation cost; according to the objective function and required constraint conditions, constructing a multi-cycle energy and mass coupling model; and optimizing the multi-cycle energy and mass coupling model based on an optimization algorithm fusing branches and subduction. Production and energy consumption are balanced, and economic benefits of the system are improved.

Description

Technical Field

[0001] This application mainly relates to the field of refinery operation optimization, and in particular to a multi-cycle optimization method, device and storage medium for an energy-mass coupling system. Background Technology

[0002] As the core conversion unit for the transformation of heavy oil into lighter oil, the catalytic cracking unit's large-scale production characteristics result in energy consumption accounting for 23% to 35% of the plant's total energy consumption. The steam system, as the refinery's energy hub, undertakes the conversion and transmission of multiple energy flows—fuel, steam, and electricity. Its four-stage pressure architecture (ultra-high pressure / high pressure / medium pressure / low pressure) and the catalytic cracking unit cluster exhibit significant energy-mass interaction: the reaction-regeneration unit relies on the steam system for process steam (stripping, atomization), and the high-pressure steam generated from the waste heat recovery of regenerated flue gas feeds back into the steam network, forming a two-way energy-mass cycle. This cross-system coupling relationship requires the optimization model to simultaneously accommodate mechanistic constraints and economic objectives, and to overcome the limitations of single-cycle static optimization. Summary of the Invention

[0003] One objective of this application is to provide a multi-cycle optimization method, apparatus, and storage medium for energy-mass coupling systems, which solves the problems of limitations in single-cycle static optimization in the prior art, the inability to balance production and energy consumption, and the impact on economic benefits.

[0004] According to one aspect of this application, a multi-cycle optimization method for an energy-mass coupling system is provided, applicable to catalytic cracking unit groups and steam systems, the method comprising:

[0005] A component model of the catalytic cracking process is constructed based on the relevant parameters of each unit in the catalytic cracking unit group;

[0006] Construct a component model of the steam system based on the relevant parameters of each device in the steam system, and determine the required constraints.

[0007] Based on the component model of the catalytic cracking process, the component model of the steam system, and the required constraints, an objective function for the energy-mass coupling system is established, wherein the objective function aims to minimize the total operating cost.

[0008] Construct a multi-period energy-mass coupling model based on the objective function and the required constraints; and

[0009] The multi-cycle energy-mass coupling model is optimized based on an optimization algorithm that combines fusion branching and reduction.

[0010] Optionally, the component model of the catalytic cracking process includes a catalytic cracking regenerator model and a catalytic cracking flue gas heat recovery unit model. The construction of the component model of the catalytic cracking process based on the relevant parameters of each unit in the catalytic cracking unit group includes:

[0011] A multiple regression equation was constructed based on the flue gas volume in the catalytic cracking regenerator, the amount of coke produced by the reaction, the temperature of each section of the regenerator, and the main regeneration air volume.

[0012] Determine the operational constraints, and construct a catalytic cracking regenerator model based on the operational constraints and the multiple regression equation;

[0013] The high-pressure steam quantity equation is determined based on the flue gas volume, temperature before and after heat recovery, carbon monoxide combustion heat, and high-pressure steam enthalpy of the catalytic cracking flue gas heat recovery unit. This high-pressure steam quantity equation is then used as the model for the catalytic cracking flue gas heat recovery unit.

[0014] Optionally, the component model of the steam system includes a boiler model, a steam turbine model, a motor model, and a desuperheating and pressure reducing valve model. Constructing the component model of the steam system based on the relevant parameters of each device in the steam system includes:

[0015] A boiler model is constructed based on the steam parameters required by the boiler unit, historical operating data, and equipment characteristic information.

[0016] The turbine category is determined based on the turbine's operating characteristics and functional information, and a turbine model is constructed based on the parameters required for the turbine category.

[0017] The power output function of the equipment is determined based on the rated operating power of the motor and the drive mode selection, and the motor model is determined based on the constraints of electric drive and the power output function of the equipment.

[0018] The thermodynamic parameter set is determined based on the real-time acquired process data. The mass flow rate relationship between the inlet and outlet of the periodic working fluid is determined based on the thermodynamic parameter set. The desuperheating and pressure reducing valve model is determined based on the mass flow rate relationship and water volume constraints.

[0019] Optionally, a boiler model is constructed based on the steam parameters required by the boiler unit, historical operating data, and equipment characteristic information, including:

[0020] Construct an equation for the total steam production of the boiler during the cycle based on the steam parameters required by the boiler unit.

[0021] The boiler thermal efficiency characteristic equation is constructed based on the total steam production equation, historical operating data, and equipment characteristic information.

[0022] Based on the boiler thermal efficiency characteristic equation and the mass conservation and energy conversion relationship of the boiler unit, an analytical function of the boiler thermal system is constructed, wherein the analytical function of the boiler thermal system is used to characterize the dynamic coupling relationship between fuel input and steam output;

[0023] The boiler model is determined based on the analytical function of the boiler thermal system and the operating constraints of the boiler unit.

[0024] Optionally, the turbine type includes extraction-condensing and back-pressure turbines, and the step of constructing a turbine model according to the required parameters corresponding to the turbine type includes:

[0025] The shaft power function of the extraction-condensing steam turbine is constructed based on the periodic extraction steam mass flow rate, the number of separable operating units of the unit, and the flow balance parameter set. The working fluid energy state constraint is defined by the process parameters. The extraction-condensing steam turbine model is constructed based on the shaft power function of the extraction-condensing steam turbine and the working fluid energy state constraint.

[0026] Based on the input mass flow rate of the working fluid and the thermodynamic state parameter set, a back-pressure mechanical shaft-end output power function is constructed to obtain a back-pressure steam turbine model.

[0027] Optionally, the required constraints include: constraints for the first steam level, and the method further includes:

[0028] The constraints for the first steam level are constructed based on the steam flow rate of the first steam level generated in the cycle, the steam inlet steam flow rate of the first steam level turbine, and the steam flow rate of the first-level steam entering the desuperheating and pressure reducing valve to convert it into the second steam level.

[0029] Optionally, the required constraints include constraints for the second steam level and constraints for the third steam level, and the method further includes:

[0030] The constraints for the current steam level are constructed based on the following: the steam extraction flow rate of the turbine converting steam from the previous steam level to the current steam level; the steam flow rate of the current steam level exiting the desuperheating and pressure reducing valve; the steam flow rate of the current steam level generated by the energy recovery unit in the catalytic cracking unit group; the process steam sales flow rate of the current steam level; and the steam flow rate of the current steam level entering the desuperheating and pressure reducing valve to convert it into the next steam level. The current steam level includes either the second steam level or the third steam level.

[0031] Optionally, the required constraints include constraints for the fourth steam level, and the method further includes:

[0032] The constraints for the fourth steam class are determined based on the steam outflow rate of the second steam class turbine, the steam outflow rate of the third steam class turbine, the steam flow rate at the desuperheating and pressure reducing valve of the fourth steam class, and the process steam sales flow rate of the fourth steam class.

[0033] Optionally, establishing the objective function of the energy-mass coupling system based on the component model of the catalytic cracking process, the component model of the steam system, and the required constraints includes:

[0034] Based on the component model of the catalytic cracking process, the raw material purchase cost of each unit in the catalytic cracking unit group and the environmental treatment cost during the production process of the catalytic cracking unit group are determined, and the total cost of the catalytic cracking process is obtained.

[0035] Based on the component model of the steam system and the required constraints, determine the boiler fuel consumption and purchased water supply costs, pressure reducing valve process water consumption costs, motor equipment energy consumption costs, and environmental treatment costs of the boiler during the production process, and obtain the total cost of the steam system.

[0036] The benefit information of the energy-mass coupling system is determined based on the component model of the catalytic cracking process and the component model of the steam system.

[0037] Based on the revenue information of the energy-mass coupling system, the total cost of the catalytic cracking process, and the total cost of the steam system, an objective function is established to minimize the total operating cost.

[0038] Optionally, the optimization algorithm based on fusion branching and reduction optimizes the multi-cycle energy-mass coupling model, including:

[0039] The multi-cycle energy-mass coupling model is integrated with the catalytic cracking unit group and steam system of an actual refinery as the empirical object.

[0040] Global optimization calculations are performed based on the fusion branch and reduction optimization algorithm, and the parameters of the obtained optimal solution are combined to adjust the corresponding parameters in the multi-cycle energy-mass coupling model.

[0041] According to another aspect of this application, a multi-period optimization device for an energy-mass coupling system is also provided, the device comprising:

[0042] One or more processors; and

[0043] A memory storing computer-readable instructions, which, when executed, cause the processor to perform operations as described above.

[0044] According to another aspect of this application, a computer-readable medium is also provided, having stored thereon computer instructions that can be executed by a processor to implement the methods described above.

[0045] Compared with existing technologies, this application constructs a component model of the catalytic cracking process based on the relevant parameters of each unit in the catalytic cracking unit group; constructs a component model of the steam system based on the relevant parameters of each unit in the steam system, and determines the required constraints; establishes an objective function for the energy-mass coupling system based on the component models of the catalytic cracking process, the steam system, and the required constraints, wherein the objective function aims to minimize the total operating cost; constructs a multi-cycle energy-mass coupling model based on the objective function and the required constraints; and optimizes the multi-cycle energy-mass coupling model based on a fusion branching and reduction optimization algorithm. This balances production and energy consumption, improving the system's economic efficiency. Attached Figure Description

[0046] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the specific embodiments of this application will be described in detail below with reference to the accompanying drawings, wherein:

[0047] Figure 1 A schematic flowchart of a multi-period optimization method for an energy-mass coupling system according to one aspect of this application is shown.

[0048] Figure 2 This invention provides a schematic diagram illustrating the fitting effect of various products from catalytic cracking in one embodiment of the present application.

[0049] Figure 3 This diagram illustrates a comparison of the optimization results of a multi-cycle steam system before and after optimization in one embodiment of this application.

[0050] Figure 4 This diagram illustrates a comparison of the optimization results of the FCC1 product before and after optimization in a multi-cycle energy-mass coupling system according to an embodiment of this application.

[0051] Figure 5 This diagram illustrates a comparison of the FCC2 product optimization results before and after optimization in a multi-cycle energy-mass coupling system according to an embodiment of this application.

[0052] Figure 6 This diagram illustrates a comparison of the economic benefits of different models before and after optimization in single-cycle and multi-cycle scenarios in one embodiment of this application.

[0053] Figure 7 This diagram illustrates a system block diagram of a multi-cycle optimization device for an energy-mass coupling system according to an embodiment of this application.

[0054] The same or similar reference numerals in the accompanying drawings represent the same or similar parts. Detailed Implementation

[0055] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the specific embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0056] Many specific details are set forth in the following description in order to provide a full understanding of this application. However, this application may also be implemented in other ways different from those described herein, and therefore this application is not limited to the specific embodiments disclosed below.

[0057] As indicated in this application and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" are not specifically singular and may include plural forms. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of explicitly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0058] To address the limitations of single-cycle models, this application proposes a multi-cycle energy-mass coupling modeling method for catalytic cracking unit clusters and steam systems, integrating the operating mechanisms of the cracking reaction and the steam system. First, unit models of the catalytic cracking unit are independently constructed through historical operating data fitting and parameter identification, selecting key state variables and analyzing the intrinsic relationship between product distribution and steam demand. Second, based on the operating characteristics of the steam system equipment, mechanistic models of key equipment in the steam network (boiler, turbine, pressure reducing valve) are established, quantifying the energy loss and economic cost of the steam cascade utilization process. Component models of the production process are constructed, including models of the catalytic cracking reactor, regenerator, flue gas heat recovery unit, and models of the steam system's boiler, turbine, motor, desuperheating and pressure reducing valves, considering steam head constraints at different steam levels. Based on this, considering seasonal variations in steam supply, an energy-mass coupling network model of the catalytic cracking unit cluster and steam system is constructed, forming a mixed-integer linear programming (MILP) framework optimized for different seasons.

[0059] The optimization model aims to maximize the overall economic benefits of the plant, integrating multi-dimensional economic factors: the revenue component includes sales revenue from catalytic cracking products and revenue from the sale of steam at all levels; the cost component includes raw material procurement costs, steam system operating costs (fuel / water / electricity consumption), and carbon emission control costs. The model strictly constrains equipment operating boundaries, energy-mass balance equations, and discrete logic relationships. Industrial case studies have validated that the proposed method can effectively improve efficiency and energy efficiency in actual refineries.

[0060] The specific plan is as follows:

[0061] Figure 1 This diagram illustrates a multi-cycle optimization method for an energy-mass coupling system according to one aspect of this application, applied to a catalytic cracking unit group and a steam system. The method includes steps S11 to S15, wherein...

[0062] Step S11: Construct component models of the catalytic cracking process based on the relevant parameters of each unit in the catalytic cracking unit group. Here, the relevant parameters include the amount of feedstock, reaction temperature, steam volume, amount of coke produced in the reaction, and flue gas volume. The component models of the catalytic cracking process are constructed, including a catalytic cracking reactor model, which includes a catalytic cracking regenerator model and a catalytic cracking flue gas heat recovery unit model.

[0063] Step S12: Construct a component model of the steam system based on the relevant parameters of each device in the steam system, and determine the required constraints. Here, the relevant parameters of each device include the operating parameters, operating status, equipment characteristics, etc., of each device. Construct a component model of the steam system, including a boiler model (BO), a steam turbine model (ET), a motor model, and a desuperheating and pressure reducing valve model (LV).

[0064] Step S13: Based on the component models of the catalytic cracking process, the steam system, and the required constraints, establish the objective function for the energy-mass coupling system, wherein the objective function aims to minimize the total operating cost. Here, constraints are imposed on the steam heads of different steam grades, including ultra-high pressure steam (SS), high pressure steam (HS), medium pressure steam (MS), and low pressure steam (LS). Based on the constructed component models, consider the economic benefits and cost system to establish an objective function for maximizing the economic benefits of the energy-mass coupling system, which aims to minimize the overall operating cost of the system.

[0065] Step S14: Construct a multi-cycle energy-mass coupling model based on the objective function and the required constraints. Here, based on the objective function of maximizing economic benefits, the models of each component, and the corresponding constraints to be satisfied, construct a multi-cycle energy-mass coupling model of the catalytic cracking unit group and the steam system.

[0066] Step S15: Optimize the multi-cycle energy-mass coupling model based on the fusion branch and reduction optimization algorithm. Here, a multi-cycle energy-mass coupling optimization framework is proposed based on the fusion branch and reduction optimization algorithm to perform global optimization calculations, obtain the optimal solution, and adjust the relevant parameters in the multi-cycle energy-mass coupling model according to the optimal parameter combination corresponding to the optimal solution. This adjusts the operating conditions, balances production and energy consumption, and improves the system's economic efficiency.

[0067] In one embodiment of this application, the energy-mass coupling optimization problem of a catalytic cracking unit and a steam system is described. The catalytic cracking unit process system consists of a reaction-regeneration system, a fractionation system, an absorption stabilization system, a flue gas treatment system, and an energy recovery system. This application mainly considers the reaction-regeneration system, the flue gas treatment system, and the energy recovery system. In the reaction-regeneration unit, the feedstock oil is accelerated by pre-lift steam and atomized steam (medium-pressure steam) and then forms a turbulent fluidized system with the high-temperature regeneration catalyst in the riser reactor, completing a deep cracking reaction within the optimized temperature range of 500–520°C. The deactivated catalyst, after desorption of oil and gas in the stripping section, enters the regenerator, where it is regenerated through coke combustion in an oxygen-rich environment. The released chemical energy constitutes the main heat source of the system. After the reaction products are cut by the fractionation tower, the following components are produced: a gas-rich component at the top of the tower (gasoline, liquefied petroleum gas, and dry gas), a diesel component on the side of the tower, and a heavy oil slurry component at the bottom of the tower.

[0068] The regenerated flue gas waste heat boiler produces high-pressure steam, which is then degraded into HS / MS steam for power generation via an extraction turbine (ET) and supplied to the plant. Stripped steam and atomized steam from the reaction system constitute the main steam demand points. The steam network achieves dynamic balance through a letdown valve (LV), a backpressure turbine (BT), and a condensate recycling system. 30-40% of the SS steam is used to drive power equipment, 20-25% is depressurized to meet process requirements, and the remainder is sold. The system condensate is deaerated and returned to the boiler, forming a closed-loop system.

[0069] In this embodiment, the steam demand at each stage of the refinery is sold externally, and the revenue constitutes an important source of income. This two-way energy-mass interaction significantly increases the system complexity, involving the coordinated optimization of continuous operational variables (such as steam flow rate and reaction temperature) and discrete decision variables (such as equipment start-up and shutdown status), forming a typical large-scale mixed integer programming problem.

[0070] The relevant parameters in this application include known parameters and decision variables. Known parameters include economic parameters, equipment parameters, and physical property parameters. Economic parameters include the selling price of catalytic cracking products (gasoline, diesel, liquefied petroleum gas, etc.); the selling price of fourth-stage steam; feedstock cost; fuel procurement; and carbon emission treatment cost. Equipment parameters include the upper and lower operating limits of the boiler (BO), turbine (ET / BT), and pressure reducing valve (LV), as well as the unit model parameters and variable operating limits of the catalytic cracking unit. Physical property parameters include the pressure and temperature characteristics of different grades of steam. Decision variables include purchased quantities, process parameters, steam system parameters, and motor drive mode. Purchased quantities include the purchased quantities of fuel and feedstock to meet the production needs of the catalytic cracking unit. Process parameters include the operating variables of the catalytic cracking unit, the demand for medium-pressure steam, and the output of high-pressure steam. Steam system parameters include: the fuel consumption of BO, the output of ultra-high-pressure steam produced, the steam inlet and extraction rates of ET, and the steam inlet flow rate of LV to ensure the stable operation and optimization of the steam system. Motor drive mode includes the choice between motor / turbine drive (a binary variable).

[0071] In one embodiment of this application, in step S11, a multiple regression equation is constructed based on the flue gas volume in the catalytic cracking regenerator, the amount of coke produced by the reaction, the temperature of each section of the regenerator, and the main regeneration air volume; operational constraints are determined, and a catalytic cracking regenerator model is constructed based on the operational constraints and the multiple regression equation; a high-pressure steam volume equation is determined based on the flue gas volume, temperature before and after heat recovery, carbon monoxide combustion heat, and high-pressure steam enthalpy of the catalytic cracking flue gas heat recovery unit, and the high-pressure steam volume equation is used as the model of the catalytic cracking flue gas heat recovery unit. Here, in the catalytic cracking reactor, the feedstock oil is injected into the reactor through the bottom of the riser, and achieves full gas-solid contact with the regeneration catalyst in the turbulent flow field formed by the pre-riser steam (medium-pressure steam). At a reaction temperature of 500–520°C, the feedstock oil molecules undergo a full cracking reaction. The reaction products are separated by cyclone separators and enter the fractionation system, where they are cut into oil slurry, diesel, gasoline, liquefied petroleum gas, and dry gas according to their boiling range differences, and coke is deposited as a byproduct on the catalyst surface. The production volumes of slurry oil, diesel oil, gasoline, dry gas, and liquefied petroleum gas have actual measured data. However, the amount of coke cannot be directly obtained in the reaction regeneration closed-loop system and must be calculated from the composition of the regenerator flue gas.

[0072]

[0073]

[0074] Coke = C w +H w (4)

[0075] Among them, V A The main air volume for wet conditions (m³ / h); yCO , Dry basis flue gas integral (%); The humidity of air molecules; C w Carbon content (t / h); H w Hydrogen quantity (t / h); Coke quantity (t / h).

[0076] The amount of raw materials, reaction temperature, and steam volume during the reaction process directly affect the product distribution, thus impacting the final economic benefits. Using a linear fitting method, equation (5) is constructed to simplify the relationship between product yield and the operating variables:

[0077] y i =α1F feed +α2T reactor +α3S steam +α4 (5)

[0078] Operational constraints include:

[0079] 150≤F feed ≤160

[0080] 500≤T reactor ≤520

[0081] 0.15F feed ≤S steam ≤0.25F feed (6)

[0082] Among them, y i For the yield (t / h) of each product from catalytic cracking, F feed T represents the raw material feed rate (t / h). reactor S represents the reactor reaction temperature (°C). steam The required steam volume (t / h) for the reaction is given, and α1, α2, α3, and α4 are correlation coefficients. The fitting results are as follows: Figure 2 As shown.

[0083] Deactivated catalyst in the catalytic cracking process is separated and fed into a regenerator. The surface coke material comes into contact with oxygen in the air and combusts, yielding a regenerated catalyst, which is then reintroduced into the reactor. The coke combustion process generates a large amount of heat, which needs to be recovered. It also produces significant amounts of environmentally harmful gases such as nitrogen oxides, sulfur dioxide, carbon monoxide, and carbon dioxide. Variations in coke quantity and regenerator operating conditions lead to changes in flue gas volume; therefore, multiple regression was used to establish the following relationship:

[0084] Q fluegas =k1y Coke +k2T r1 +k3T r2+k4P r1 +k5P r2 +k6W air +k7 (7)

[0085] Among them, Q fluegas For flue gas volume (t / h), y Coke T represents the amount of coke produced in the reaction (t / h). r1 T r2 P represents the temperatures (°C) of the first and second stages of the regenerator. r1 P r2 The pressure (kPa) of the first and second stages of the regenerator, W air The regenerated main air volume is represented by k1, k2, k3, k4, k5, k6, and k7, which are corresponding coefficients.

[0086] In the regenerator heat balance, the heat of the regenerated flue gas mainly comes from the heat released by coking, the heat of the main air temperature rise, and the heat of coke desorption. The amount of coke, the temperatures of the first and second stages of the regenerator, and the main air volume of the regenerator are selected as influencing variables, and the following relationship is constructed:

[0087] T fluegas =g1y Coke +g2T r1 +g3T r2 +g4W air +g5 (8)

[0088] Among them, T fluegas Let y be the flue gas temperature (°C). Coke T represents the amount of coke produced in the reaction (t / h). r1 T r2 The temperatures (°C) of the first and second stages of the regenerator, W air The regenerated main air volume is (Nm3 / min), and g1, g2, g3, g4, and g5 are the corresponding coefficients.

[0089] The amounts of carbon monoxide and carbon dioxide in the flue gas are:

[0090]

[0091] V CO =μ2y Coke ·(1-η comb (10)

[0092] in, V CO η represents the amount of carbon monoxide and carbon dioxide (t / h). μ1 and μ2 are the formation coefficients of carbon monoxide and carbon dioxide, respectively. comb η represents the complete combustion efficiency of coke in the regenerator, i.e., the proportion of carbon in the coke converted into carbon dioxide. Its value ranges from 0 to η. comb ≤1.

[0093] Operational constraints include:

[0094] 600≤T r1 ≤780

[0095] 600≤T r2 ≤780

[0096] 150≤P r1 ≤300

[0097] 150≤P r2 ≤300

[0098] 1500≤W air ≤2300 (11)

[0099] In the catalytic cracking flue gas heat recovery unit model, high-temperature flue gas (650–700℃) enters the CO incinerator after undergoing three-stage cyclone separation (dust removal efficiency >99.9%). Complete CO oxidation is achieved under excess air coefficient conditions of 1.1–1.3. The reaction equation is as follows:

[0100] CO+0.5O2→CO2(ΔH=-283.0kJ / mol) (12)

[0101] In this process, the heat from the regenerated flue gas and the heat generated by carbon monoxide combustion are recovered through circulating water to produce high-pressure steam, which then enters the steam pipeline network. It is assumed that there is no energy loss during the heat recovery process.

[0102]

[0103] Wherein, SFCC represents the amount of high-pressure steam (t / h) generated by the flue gas heat recovery unit of the catalytic cracking unit, and Q... fluegas T represents the flue gas volume (t / h). fluegas T represents the temperature of the flue gas before heat recovery (°C). out Q represents the temperature (°C) after heat recovery. CO =V CO 12.63 MJ / Nm 3 For CO combustion heat, ΔH vap =3300kJ / kg is the enthalpy of high-pressure steam.

[0104] In one embodiment of this application, the component model of the steam system includes a boiler model, a steam turbine model, a motor model, and a desuperheating and pressure reducing valve model. In step S12, the boiler model is constructed based on the steam parameters required by the boiler device, historical operating data, and equipment characteristic information; the steam turbine category is determined based on the operating characteristics and functional information of the steam turbine, and the steam turbine model is constructed based on the parameters required for the steam turbine category; the equipment power output function is determined based on the rated operating power of the motor and the drive mode selection, and the motor model is determined based on the constraints of electric drive and the equipment power output function; the thermodynamic parameter set is determined based on the real-time acquired process data, the mass flow rate relationship between the inlet and outlet of the periodic working fluid is determined based on the thermodynamic parameter set, and the desuperheating and pressure reducing valve model is determined based on the mass flow rate relationship and water quantity constraints.

[0105] Specifically, when constructing the boiler model, the total steam production equation of the boiler within a cycle is constructed based on the steam parameters required by the boiler unit; the boiler thermal efficiency characteristic equation is constructed based on the total steam production equation, historical operating data, and equipment characteristic information; the boiler thermal efficiency characteristic equation and the mass conservation and energy conversion relationship of the boiler unit are used to construct the boiler thermal system analytical function, wherein the boiler thermal system analytical function is used to characterize the dynamic coupling relationship between fuel input and steam output; and the boiler model is determined based on the boiler thermal system analytical function and the operating constraints of the boiler unit.

[0106] To reduce the high costs associated with treating carbon emissions from coal combustion, refineries prioritize natural gas as boiler fuel. The boilers convert circulating water and external makeup water within the refining unit into high-pressure steam with specific parameters through thermal energy conversion, as shown in equation (14). Considering the periodic fluctuations in steam load, the process system reintroduces condensate recovered from previous production cycles as a renewable resource into the thermal cycle, achieving cross-cycle cascade utilization. This model assumes that the heat transfer efficiency remains ideal throughout the phase change process in the condensate-steam conversion stage.

[0107]

[0108] Among them, SB bo,t RCW represents the total steam output (t / h) of the boiler during cycle t. bo,t-1 For the circulating water flow rate (t / h) recovered in cycle t-1, EPW bo,t The amount of water purchased externally during cycle t (t / h) is used for replenishment.

[0109] The boiler provides ultra-high-pressure steam to the steam network through fuel combustion, and its efficiency model is constructed based on historical operating data and equipment characteristics.

[0110]

[0111] Where, η bo,t Characterizes the boiler's thermal efficiency. Parameter α bo β bo The core coefficients constituting the boiler operating state regression equation are obtained analytically through a polynomial fitting model constructed by integrating historical operating data and equipment thermodynamic characteristics. Characterizes the maximum processing capacity (t / h) of the steam generation system.

[0112] The relationship between mass conservation and energy conversion in a boiler unit is characterized by equation (16):

[0113]

[0114] Among them, H SS and H RCW F represents the thermodynamic enthalpy characteristics of ultra-high pressure steam, circulating cooling medium, and boiler inlet water unit, respectively. bo,t Q represents the fuel consumption intensity (t / h) of the t-th operating cycle. fuel Indicators characterizing fuel energy density (MJ / t).

[0115] By combining equations (14) and (15) and substituting them into equation (16), an analytical expression for the boiler thermodynamic system that characterizes the dynamic coupling relationship between fuel input and steam output can be derived. Its complete mathematical description is shown in equation (17):

[0116]

[0117] During the actual operation of the steam power system, each process equipment must be strictly limited to operating within the design operating range.

[0118]

[0119] in, and These are the minimum and maximum flow rates (t / h) of the boiler.

[0120] Specifically, when constructing the turbine model, the shaft power function of the extraction-condensing turbine is constructed based on the periodic extraction steam mass flow rate, the number of divisible operating units of the unit, and the flow balance parameter set. The working fluid energy state constraint is defined by the process parameters. The extraction-condensing turbine model is constructed based on the extraction-condensing turbine shaft power function and the working fluid energy state constraint. The back-pressure mechanical shaft-end output power function is constructed based on the working fluid input mass flow rate and the thermodynamic state parameter set to obtain the back-pressure turbine model.

[0121] Based on their operating characteristics and functional differences, steam turbines can be divided into two main categories: extraction-condensing (ET) and back-pressure (BT). In the modeling process, a modular system decomposition strategy was adopted. This modeling method uses the isentropic efficiency assumption as the basic theoretical framework, decomposing a single multi-stage steam turbine into several distinctive unit turbine units to accurately adapt to diverse steam parameter requirements. Combining historical operating data and equipment operating mechanisms, equipment models according to formulas (19) to (21) were constructed. Finally, the least squares estimation algorithm was applied for numerical fitting optimization, as shown in the following formulas:

[0122]

[0123]

[0124] Among them, PET et,t Characterizes the total shaft power of the steam turbine in the t-th operating cycle. Characterized by the corresponding cycle extraction steam mass flow rate (t / h), I is the number of detachable operating units of the unit, and the flow balance parameter set includes the inlet steam flow rate MET. et,in,t With exhaust steam flow rate MET et,exh,t (t / h), MET et,in,t and MET et,exh,t As a dynamically adjustable variable, the thermodynamic parameter set consists of the inlet enthalpy H. et,in extraction enthalpy And exhaust enthalpy H et,exh Composition, providing a complete description of the changes in the energy state of the working fluid.

[0125]

[0126] The working fluid input constraints of ET are defined by process parameters: The rated upper limit (t / h) characterizing the inlet steam flow rate. The minimum input threshold (t / h) allowed by the characterization process. Correspondingly, the dynamic adjustment range of the boiler steam output capacity is determined by the maximum continuous evaporation rate. and minimum steam production Two critical parameters limit (t / h).

[0127] The operation mechanism of the steam power system ET in a refining and chemical enterprise is explained using a dual steam turbine architecture as an example. The shaft power output characteristics of BT are mathematically quantified by equation (24):

[0128]

[0129] Among them, PBT bt,t Characterizing the output power of the BT mechanical shaft end, MBT bt,in,tCharacterizes the input mass flow rate (t / h) of the working fluid. The thermodynamic state parameter set includes the inlet working fluid specific enthalpy H. bt,in Enthalpy H relative to the export working fluid bt,out (MJ / kg), which fully describes the energy conversion characteristics of a steam turbine system.

[0130] To optimize operating costs, the power source configuration for motors or compressors offers the option of both steam-driven and electric-driven operation. During modeling, the drive unit is assumed to operate stably under rated conditions after power switching, and the effects of unsteady-state conditions and abnormal operating states are not considered. The mathematical description of this power system is fully represented by a set of equations (25) to (27).

[0131]

[0132] σ m,t ·(1-σ m,t )=0 (27)

[0133] Among them, PBT bt,t and PEY ey,t PM represents the power output of the equipment in steam-driven and electric-driven modes respectively during period t. m Characterizes the rated operating power of the motor. Introduces a binary decision variable σ. m,t Construct a driver mode selection mechanism: when σ m,t When σ = 1, the system adopts an electric drive scheme; otherwise, σ = 1. m,t When =0 is 0, the system switches to steam-driven mode.

[0134] The steam network of the refining and chemical plant features multi-pressure level working fluid coupling. Its operating mechanism can be analyzed as follows: BO, as the core steam source, continuously outputs ultra-high pressure working fluid, while ET achieves steam diversion through extraction during energy conversion. When a mismatch occurs in the pipeline steam pressure-energy level matching, the system uses LV to adjust the working fluid parameters, meeting the low-pressure steam demand through throttling and pressure reduction.

[0135] The mathematical representation model of LV is constructed based on the thermodynamic conservation law and the principle of mass-energy double conservation, and its complete mathematical expression is shown in equation (28):

[0136]

[0137] Among them, MLV lv,in,t and MLV lv,out,t Let MLV represent the inlet and outlet mass flow rates (t / h) of the working fluid during the t-th operating cycle. lv,in,t This is a dynamic adjustment coefficient. The thermodynamic parameter set includes the inlet vapor specific enthalpy H. lv,in Specific enthalpy of outlet steam H lv,outand the specific enthalpy H of the makeup water lv,w (MJ / kg). Model characteristic parameter H lv,in H lv,w and H lv,out The numerical values ​​are used to identify the system through a least squares algorithm. This algorithm deeply integrates prior knowledge such as the historical operating dataset of the equipment and the flow characteristics of the working fluid inside the valve, and finally analyzes and obtains a combination of parameters with engineering physical significance. (H) lv,in -H lv,w ) / (H lv,out -H lv,w ξ is obtained through real-time process data analysis. lv The operational relationship and water quantity constraint are mathematically quantified through equations (29) and (30).

[0138]

[0139]

[0140] Among them, MLV lvowr,t Characterizes the liquid water replenishment flow rate (t / h) during the t-th operating cycle. The upper limit of the process allowable flow rate (t / h) of the steam working fluid input during the same cycle.

[0141] In one embodiment of this application, to achieve cascaded energy utilization, the fractionated steam output from the process section needs to be connected to the corresponding pressure level node in the pipeline network according to its thermodynamic properties. The inlet working fluid flow rate of each node needs to be precisely matched with the total downstream load. To this end, a dynamic coupling model of the pipeline network nodes based on the mass and energy conservation relationship needs to be established, and the thermal balance of the system is ensured through multi-variable collaborative regulation.

[0142] The required constraints include: constraints for the first steam level. The method further includes: constructing constraints for the first steam level based on the steam flow rate of the first steam level generated periodically, the steam inlet flow rate of the first steam level turbine, and the steam flow rate of the first-level steam entering the desuperheating and pressure-reducing valve to be converted into the second steam level. Here, the first steam level is ultra-high pressure steam (SS). For the ultra-high pressure steam flow network, the balance formula is as shown in equation (31):

[0143]

[0144] Among them, SBO bo,t MET represents the ultra-high pressure steam flow rate generated by the boiler in cycle t. et,in,t For the steam inlet steam flow rate of SS-level ET, MLV lv1,in,t The ultra-high pressure steam enters the desuperheating and pressure reducing valve and is converted into high pressure steam flow.

[0145] In one embodiment of this application, the required constraints include constraints for the second steam level and constraints for the third steam level. The method further includes: constructing constraints for the current steam level based on the steam extraction flow rate of the turbine converting steam of the previous steam level into steam of the current steam level, the steam flow rate of the current steam level exiting the desuperheating and pressure reducing valve, the steam flow rate of the current steam level generated by the energy recovery unit in the catalytic cracking unit group, the process steam sales flow rate of the current steam level, and the steam flow rate of the current level entering the desuperheating and pressure reducing valve to convert into steam of the next steam level. The current steam level includes either the second or third steam level. Here, the second steam level is high-pressure steam (HS), and the third steam level is medium-pressure steam (MS). For the high-pressure steam flow network, the equilibrium formula for the constraints is as shown in equation (32):

[0146]

[0147] Among them, MET et,ext,t MLV is the steam extraction flow rate used by ET to convert ultra-high pressure steam into high pressure steam. lv1,out,t For the high-pressure steam outlet desuperheating and pressure reducing valve flow rate, SFCC t MBT is used to measure the high-pressure steam flow generated by the energy recovery unit of the catalytic cracking unit group. HSbt,in,t For the steam inlet steam flow rate of HS-grade BT, NSP HS,t It is the process steam sales flow rate of HS grade, MLV lv2,in,t The high-pressure steam is converted into medium-pressure steam flow by the desuperheating and pressure reducing valve.

[0148] For medium-pressure steam flow pipeline networks, the equilibrium formula for the constraint conditions is as shown in equation (33):

[0149]

[0150] Among them, MET et,ext,t MBT is the steam extraction flow rate for converting ultra-high pressure steam into medium pressure steam by ET. HSbt,out,t For HS-grade BT, the steam outflow rate is MLV. lv2,out,t For medium-pressure steam outlet desuperheating and pressure reducing valve flow rate, MBT MSbt,in,t For the steam inlet flow rate of MS-level BT, NSP MS,t It is the process steam sales flow rate at the MS level, MLV lv3,in,t To convert medium-pressure steam into low-pressure steam flow through the desuperheating and pressure-reducing valve, S steam,t This represents the total medium-pressure steam flow rate required for the reactor units of the catalytic cracking unit group.

[0151] In one embodiment of this application, the required constraints include constraints for the fourth steam class. The method further includes: determining the constraints for the fourth steam class based on the steam outflow of the turbine of the second steam class, the steam outflow of the turbine of the third steam class, the steam flow rate at the desuperheating and pressure reducing valve of the fourth steam class, and the process steam sales flow rate of the fourth steam class. The fourth steam class is low-pressure steam. For a low-pressure steam flow network, the balance formula for the constraints is as shown in equation (34):

[0152]

[0153] Among them, MBT HSbt,out,t For HS-grade BT, the steam outflow rate is MBT. MSbt,out,t For MS-level BT steam outflow rate, MLV lv3,out,t For low-pressure steam outlet desuperheating and pressure reducing valve flow rate, NSP LS,t It is the process steam sales flow rate at the LS level.

[0154] In one embodiment of this application, in step S13, the raw material purchase cost of each unit in the catalytic cracking unit group and the environmental treatment cost during the production process of the catalytic cracking unit group are determined based on the component model of the catalytic cracking process, thus obtaining the total cost of the catalytic cracking process; the boiler fuel consumption and purchased water supply cost, pressure reducing valve process water consumption cost, motor equipment energy consumption cost, and boiler environmental treatment cost during the production process are determined based on the component model of the steam system and the required constraints, thus obtaining the total cost of the steam system; the revenue information of the energy-mass coupling system is determined based on the component model of the catalytic cracking process and the component model of the steam system; and an objective function is established based on the revenue information of the energy-mass coupling system, the total cost of the catalytic cracking process, and the total cost of the steam system, with the goal of minimizing the total operating cost. Here, the objective function aims to maximize the economic benefits of the energy-quality coupling system of the catalytic cracking unit group and the steam system. Taking into account the product benefits and environmental impact of the coupling system, the economic benefits mainly consist of the sales revenue of the finished products of the catalytic cracking process and the sales revenue of surplus steam from the multi-stage steam pipeline network. The cost system includes the cost of raw materials purchased from the catalytic cracking unit, the cost of boiler fuel consumption and purchased water supply, the cost of process water consumption of pressure reducing valves, the comprehensive energy consumption cost of all plant motors and equipment under the dual-drive mode of electricity and steam, and the environmental treatment cost of carbon emissions generated during the production process of the boiler and the two catalytic cracking units. The constructed objective function is shown in equation (35):

[0155]

[0156] Among them, z sale To assess the economic benefits of the energy-mass coupling model of the catalytic cracking unit group and the steam system, z cost For the cost of the coupled model of the catalytic cracking unit group and the steam system, Qs,sale P represents the unit price for selling surplus steam at various levels. s For excess steam sold at all levels, Q p,sale P represents the unit price of each product from catalytic cracking. p To determine the output of each product from the catalytic cracking unit group, C rm C fuel C e C w C co2 The prices are for raw materials, fuel, electricity, water, and carbon emission treatment, respectively. feed F represents the amount of raw materials processed. bo,t PEY is the fuel required for the boiler. ey,t For the electricity consumption of the steam system, MLV lvowr,t To reduce water consumption for the temperature and pressure reducing valve, EPW bo,t For the amount of boiler water purchased externally, R co2,t V CO2,t The amount of CO2 produced by the boiler and catalytic cracking unit is represented by "benefit," which represents the total economic benefits.

[0157] In one embodiment of this application, in step S15, the multi-cycle energy-mass coupling model is integrated with the actual refinery's catalytic cracking unit group and steam system as the empirical object; global optimization calculation is performed based on the optimization algorithm of fusion branch and reduction, and the parameters of the corresponding parameters in the multi-cycle energy-mass coupling model are adjusted by combining the obtained optimal solution parameters. Here, the multi-cycle energy-mass coupling model includes an objective function and a corresponding constraint function, wherein the process model of the catalytic cracking unit includes the reactor unit model of formula (5), the regenerator unit model of formulas (7) to (10), and the flue gas heat recovery unit model of formulas (12) to (13); the process model of the steam system includes the boiler model of formulas (14) to (17), the steam turbine model of formulas (19) to (21) and formula (24), the motor model of formula (25), and the desuperheating and pressure reducing valve model of formulas (28) to (29); The optimization model of the energy-mass coupling system includes the objective function of formula (35), the constraints of the catalytic cracking process model (formulas (6) to (11)), the model constraints of the steam system process (formulas (18), (22)-(23), (26)-(27) and formula (30)), and the constraints of the multi-stage steam head (formulas (31)-(34)). The constraints of the multi-stage steam head include the ultra-high pressure steam constraint of formula (31), the high pressure steam constraint of formula (32), the medium pressure steam constraint of formula (33), and the low pressure steam constraint of formula (34). The operating variables include: catalytic cracking operating variables (F feed T reactor S steam T r1 T r2 P r1 Pr2 W air ), steam system operating variables (F) bo,t SB bo,t MET et,in,t MET et,exti,t MLV lv,in,t ), decision variable σ m,t The optimization problem in this application can be formulated as a MILP problem. The global optimal solution to the current optimization problem is obtained using a branch and prune algorithm. The process of the branch and prune algorithm is as follows:

[0158] Step 1: Initialize the model, define the catalytic cracking unit model and the steam system unit model, set the equipment operating states and operational variables, and define the objective function; Step 2: Initialize the solution parameters, obtain the active node list P, the upper bound of the branch, and the lower bound of the branch; Step 3: Select and remove a node N from the active node list P, solve the linear relaxation problem of node N, obtain the solution x and the relaxation value v. If the relaxation problem is infeasible or the relaxation value is greater than or equal to the upper bound of the branch, skip the node for pruning and continue the loop; if x is an integer feasible solution of P (satisfying all constraints), and if the relaxation value is less than the lower bound of the branch, use v as the new upper bound of the branch and x as the optimal solution, and continue the loop; generate a set of valid cut planes C violated by x, add cut plane C to the constraints of N, reprocess the node, and add N back to the active node list, selecting a non-integer variable x. i Create two child nodes N1 and N2, and set N∪{x} as the child nodes. i ≤|x i |} is N1, and N∪{x} is N1. i ≥|x i |} represents N2; return the optimal solution.

[0159] The established model integrates the FCC unit group and the entire steam system of an actual refinery as the empirical object. A MILP model is constructed using the GAMS platform, and a solver is used for global optimization calculations. To verify the applicability of the proposed optimization method in the energy-material coupling synergistic optimization of the FCC process and steam system, this study selects the FCC unit group and the entire steam system of a Chinese refining and chemical enterprise as the empirical object.

[0160] A historical process database of continuous operation data for a specific year was obtained from the target refinery. Data-driven modeling methods were used to identify parameters and construct process models for two FCC units (FCC1 and FCC2). The steam system adopts a four-level pressure rating architecture: ultra-high pressure steam SS (10.0 MPa, 505℃), HS (3.5 MPa, 374℃), MS (1.0 MPa, 255℃), and LS (0.45 MPa, 173℃). The system includes three gas-fired boilers (BO1–BO3) for SS steam production; their thermal efficiency, capacity threshold, and flow characteristics are detailed in [reference needed].

[0161] Table 1:

[0162]

[0163] Table 1

[0164] Three extraction steam turbines (ET1 to ET3) are configured, of which ET1 / ET2 extract steam to the HS pipeline network and ET3 extracts steam to the MS pipeline network;

[0165] The thermodynamic parameters and operating ranges of each ET device are listed in Table 2:

[0166]

[0167] Table 2

[0168] It also covers 15 back-pressure steam turbines (BT1~BT15), whose steam throughput, rated power and drive motor parameters are shown in Table 3:

[0169]

[0170] Table 3

[0171] All equipment parameters are cross-validated with on-site instrument calibration and design documents to ensure the engineering reliability of the model input data.

[0172] Based on long-term monitoring data collection and analysis of industrial process steam demand, it was found that the distribution of steam demand exhibits significant periodic fluctuations, with relatively small fluctuation amplitudes within a single cycle. Based on this characteristic, this study uses statistical clustering methods to select typical load profile data for each steam level and constructs a multi-cycle steam system optimization model. The model establishes a cross-cycle hydraulic correlation mechanism: the optimized return water from the ET (Energy Temperature) in each seasonal cycle flows into the next seasonal cycle, forming a recursive optimization structure with a memory effect. Simultaneously, the system dynamically balances the steam surplus, purifying the condensate generated in each cycle and returning it to the boiler feedwater system, achieving a closed-loop recycling of the heat energy medium. Figure 3As shown, dark numbers represent data before optimization, and light numbers represent data after optimization. Through optimization, the cost of the steam system in each cycle was reduced. This modeling method effectively solves the problem of fragmented boundary conditions caused by traditional single-cycle optimization, and significantly improves the engineering applicability of multi-timescale collaborative optimization.

[0173] based on Figure 3 The multi-cycle optimization results reveal significant seasonal differences in the operating characteristics of the steam system. Influenced by seasonal cyclical changes, the demand for steam sold at all levels fluctuates, and this fluctuation triggers coordinated adjustments in boiler combustion and back-pressure turbine operating loads through the energy transfer network. Simultaneously, seasonal adjustments to the reaction temperature and pressure parameters of the catalytic cracking unit cluster cause periodic fluctuations in the steam supply to the high-pressure steam network, and the demand to the medium-pressure steam network also exhibits periodic fluctuations. Specifically, during the spring cycle, the catalytic cracking unit cluster produces 322.17 tons / hour of high-pressure steam that enters the steam network, while during the winter cycle, it produces 296.75 tons / hour of high-pressure steam.

[0174] The system operating cost exhibits a stepped distribution across four typical cycles: RMB 152,392.04 / hour in spring, RMB 148,353.53 / hour in summer, RMB 151,542.79 / hour in autumn, and RMB 156,470.89 / hour in winter, with the lowest cost in summer. Further analysis shows that the cost peaks in spring and winter are mainly due to increased heating demand and increased boiler fuel consumption caused by increased process unit load, as well as higher cumulative flow of the desuperheating and pressure-reducing valves due to steam network pressure balancing requirements. Regarding motor drive optimization, due to fluctuations in steam network pressure and the load matching requirements of process units, differentiated drive mode selection occurs in different cycles. The optimized electric drive units are mainly distributed in units P10–15, with their energy-saving benefits reaching their peak in autumn.

[0175] Adjusting the operating conditions of the catalytic cracking unit group and steam system in all four seasons can effectively improve economic efficiency, as shown in Table 4:

[0176] Desuperheating and pressure reducing valve <![CDATA[ξ lv ]]> Maximum input steam flow rate (tons / hour) LV1 1.075 80.00 LV2 1.073 90.00 LV3 1.057 80.00

[0177] Table 4

[0178] From the perspective of the operating conditions of the catalytic cracking unit group, the feedstock processing volume and reaction temperature are at their maximum limits throughout the year, maximizing the output of high-value products and improving economic efficiency. Affected by the periodic fluctuations in the steam system, the production of high-pressure steam in the catalytic cracking energy recovery unit fluctuates, causing the regenerator operating conditions to change with variations in coke and flue gas volumes. Overall, optimizing operating conditions has achieved a reasonable balance between production and energy consumption, improving the system's economic efficiency.

[0179] Combination Figure 4 and 5 As shown, (a) represents the flow rate of slurry oil, (b) represents the flow rate of diesel fuel, (c) represents the flow rate of gasoline, (d) represents the flow rate of liquefied petroleum gas (LPG), (e) represents the flow rate of dry gas, and (f) represents the amount of coke. After optimization, the flow rates of high-value products (diesel, gasoline, and LPG) in both catalytic cracking units have been significantly increased. The flow rates of slurry oil and dry gas have decreased after optimization due to their lower prices. The amount of coke directly affects the high-pressure steam output of the catalytic cracking unit, and therefore changes accordingly with the steam system demand.

[0180] Combination Figure 6 As shown, (A) represents the energy-mass coupling optimization results for different cycles, and (B) represents the actual, energy-mass coupling optimized, and individual optimization results for different cycles. Under multiple cycles, the economic benefits of the spring, summer, autumn, and winter cycles were significantly improved after optimization. The optimized economic benefits for the four cycles were RMB 55,695.54 / hour, RMB 53,767.15 / hour, RMB 62,561.41 / hour, and RMB 57,948.41 / hour, respectively, representing increases of RMB 13,807.23 / hour, RMB 15,184.87 / hour, RMB 5,354.68 / hour, and RMB 11,607.15 / hour. Among them, the autumn cycle had the highest plant efficiency and steam system efficiency, but the optimization improvement was relatively small. The summer cycle had the largest improvement in economic benefits, with the plant efficiency and steam system efficiency both achieving the largest improvement, followed by the spring cycle. Comparative studies have found that, under a single cycle, the differences between cycles and the correlation between adjacent cycles are ignored, resulting in a break in the thermodynamic cycle and material balance. This leads to the least improvement in the steam system optimization throughout the year. Furthermore, with seasonal fluctuations, optimization that only considers the economic benefits of the whole year lacks certain reference value.

[0181] Subsequently, further comparative experiments on system decoupling were conducted: a local optimization strategy of "purchased medium-pressure steam - sold high-pressure steam" was implemented for the catalytic cracking unit group, while the steam system adopted a reverse balance mode of "sold medium-pressure steam - purchased high-pressure steam". Experimental data showed that the combined economic benefits of the two independent systems only increased by 23.6% compared to the actual operating baseline, while the global optimization scheme based on energy-mass coupling achieved a benefit increase of 41.2%. The economic benefits after energy-mass coupling optimization were the highest, which proves that simultaneously considering the material flow and steam energy flow of the unit can better improve economic efficiency.

[0182] In the case study, the optimized operating conditions effectively improved economic efficiency in different seasons. The reasonable adjustment of the operating conditions of the catalytic cracking unit and the steam system balanced production and energy consumption, thereby improving the system's economic efficiency. In summary, this application provides effective optimization strategies and methods for improving refinery energy efficiency.

[0183] Figure 7This diagram illustrates a system block diagram of a multi-cycle optimization device for an energy-mass coupling system according to an embodiment of this application. (See reference...) Figure 7 As shown, the multi-cycle optimization device 700 of the energy-mass coupling system may include an internal communication bus 701, a processor 702, a read-only memory (ROM) 703, a random access memory (RAM) 704, a communication port 705, and a hard disk 707. The internal communication bus 701 enables data communication between the components of the multi-cycle optimization device of the energy-mass coupling system. The processor 702 can perform judgments and issue prompts. In some embodiments, the processor 702 may consist of one or more processors.

[0184] Communication port 705 enables data transmission and communication between the multi-cycle optimization device of the energy-mass coupling system and external input / output devices. In some embodiments, the multi-cycle optimization device of the energy-mass coupling system can send and receive information and data from the network through communication port 705. In some embodiments, the multi-cycle optimization device of the energy-mass coupling system can transmit data and communicate with external input / output devices in a wired manner through input / output terminal 706.

[0185] The multi-cycle optimization device for the energy-mass coupling system may also include different forms of program storage units and data storage units, such as a hard disk 707, a read-only memory (ROM) 703, and a random access memory (RAM) 704, capable of storing various data files used for computer processing and / or communication, as well as possible program instructions executed by the processor 702. The processor 702 executes these instructions to implement the main part of the method. The results processed by the processor 702 are transmitted to an external output device via a communication port 705 and displayed on the user interface of the output device.

[0186] For example, the implementation process file of the multi-cycle optimization device for the energy-mass coupling system described above can be a computer program, stored in the hard disk 707, and can be loaded into the processor 702 for execution to implement the multi-cycle optimization method for the energy-mass coupling system of this application.

[0187] This application also provides a computer-readable medium having computer instructions stored thereon, which can be executed by a processor to implement a multi-cycle optimization method for an energy-mass coupling system as described above.

[0188] When the multi-period optimization method for energy-mass coupling systems is implemented as a computer program, it can also be stored as an article of manufacture in a computer-readable storage medium. For example, computer-readable storage media can include, but are not limited to, magnetic storage devices (e.g., hard disks, floppy disks, magnetic stripes), optical discs (e.g., compact discs (CDs), digital multifunction discs (DVDs)), smart cards, and flash memory devices (e.g., electrically erasable programmable read-only memory (EPROM), cards, sticks, key drives). Furthermore, the various storage media described herein can represent one or more devices and / or other machine-readable media used for storing information. The term "machine-readable medium" can include, but is not limited to, wireless channels and various other media (and / or storage media) capable of storing, containing, and / or carrying code and / or instructions and / or data.

[0189] It should be understood that the embodiments described above are merely illustrative. The embodiments described herein may be implemented in hardware, software, firmware, middleware, microcode, or any combination thereof. For hardware implementation, the processor may be implemented within one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, and / or other electronic units designed to perform the functions described herein, or combinations thereof.

[0190] Some aspects of this application can be executed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The aforementioned hardware or software may be referred to as a "data block," "module," "engine," "unit," "component," or "system." The processor may be one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DAPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, or combinations thereof. Furthermore, aspects of this application may manifest as computer products residing in one or more computer-readable media, including computer-readable program code. For example, computer-readable media may include, but are not limited to, magnetic storage devices (e.g., hard disks, floppy disks, magnetic tapes, etc.), optical discs (e.g., compressed CDs, digital multifunction DVDs, etc.), smart cards, and flash memory devices (e.g., cards, sticks, key drives, etc.).

[0191] A computer-readable medium may contain a propagated data signal containing computer program code, for example, on baseband or as part of a carrier wave. This propagated signal may take various forms, including electromagnetic, optical, and so on, or suitable combinations thereof. A computer-readable medium can be any computer-readable medium other than a computer-readable storage medium, which can be connected to an instruction execution system, apparatus, or device to enable communication, propagation, or transmission of a program for use. The program code located on the computer-readable medium can be propagated through any suitable medium, including radio, cable, fiber optic cable, radio frequency signals, or similar media, or any combination of the above media.

[0192] The basic concepts have been described above. Obviously, for those skilled in the art, the above disclosure is merely illustrative and does not constitute a limitation of this application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this application. Such modifications, improvements, and corrections are suggested in this application, and therefore remain within the spirit and scope of the exemplary embodiments of this application.

[0193] Furthermore, this application uses specific terms to describe embodiments of the application. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic related to at least one embodiment of the application. Therefore, it should be emphasized and noted that "an embodiment," "one embodiment," or "an alternative embodiment" mentioned twice or more in different locations in this specification do not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of the application can be appropriately combined.

[0194] In some embodiments, numbers describing the quantity of components and attributes are used. It should be understood that such numbers used in the description of embodiments are modified in some examples with the terms "approximately," "approximately," or "generally." Unless otherwise stated, "approximately," "approximately," or "generally" indicates that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may be changed depending on the characteristics required by individual embodiments. In some embodiments, numerical parameters should take into account specified significant digits and employ a general method of digit reservation. Although the numerical ranges and parameters used to confirm their breadth of scope in some embodiments of this application are approximate values, in specific embodiments, such values ​​are set as precisely as feasible.

Claims

1. A multi-cycle optimization method for an energy-mass coupling system, applied to catalytic cracking unit groups and steam systems, characterized in that, The method includes: A component model of the catalytic cracking process is constructed based on the relevant parameters of each unit in the catalytic cracking unit group; Construct a component model of the steam system based on the relevant parameters of each device in the steam system, and determine the required constraints. Based on the component model of the catalytic cracking process, the component model of the steam system, and the required constraints, an objective function for the energy-mass coupling system is established, wherein the objective function aims to minimize the total operating cost. Construct a multi-period energy-mass coupling model based on the objective function and the required constraints; and The multi-cycle energy-mass coupling model is optimized based on an optimization algorithm that combines fusion branching and reduction.

2. The method according to claim 1, characterized in that, The component model of the catalytic cracking process includes a catalytic cracking regenerator model and a catalytic cracking flue gas heat recovery unit model. The construction of the component model of the catalytic cracking process based on the relevant parameters of each unit in the catalytic cracking unit group includes: A multiple regression equation was constructed based on the flue gas volume in the catalytic cracking regenerator, the amount of coke produced by the reaction, the temperature of each section of the regenerator, and the main regeneration air volume. Determine the operational constraints, and construct a catalytic cracking regenerator model based on the operational constraints and the multiple regression equation; The high-pressure steam quantity equation is determined based on the flue gas volume, temperature before and after heat recovery, carbon monoxide combustion heat, and high-pressure steam enthalpy of the catalytic cracking flue gas heat recovery unit. This high-pressure steam quantity equation is then used as the model for the catalytic cracking flue gas heat recovery unit.

3. The method according to claim 1, characterized in that, The component model of the steam system includes a boiler model, a steam turbine model, a motor model, and a desuperheating and pressure reducing valve model. The construction of the component model of the steam system based on the relevant parameters of each device in the steam system includes: A boiler model is constructed based on the steam parameters required by the boiler unit, historical operating data, and equipment characteristic information. The turbine category is determined based on the turbine's operating characteristics and functional information, and a turbine model is constructed based on the parameters required for the turbine category. The power output function of the equipment is determined based on the rated operating power of the motor and the drive mode selection, and the motor model is determined based on the constraints of electric drive and the power output function of the equipment. The thermodynamic parameter set is determined based on the real-time acquired process data. The mass flow rate relationship between the inlet and outlet of the periodic working fluid is determined based on the thermodynamic parameter set. The desuperheating and pressure reducing valve model is determined based on the mass flow rate relationship and water volume constraints.

4. The method according to claim 3, characterized in that, A boiler model is constructed based on the steam parameters required by the boiler unit, historical operating data, and equipment characteristic information, including: Construct an equation for the total steam production of the boiler during the cycle based on the steam parameters required by the boiler unit. The boiler thermal efficiency characteristic equation is constructed based on the total steam production equation, historical operating data, and equipment characteristic information. Based on the boiler thermal efficiency characteristic equation and the mass conservation and energy conversion relationship of the boiler unit, an analytical function of the boiler thermal system is constructed, wherein the analytical function of the boiler thermal system is used to characterize the dynamic coupling relationship between fuel input and steam output. The boiler model is determined based on the analytical function of the boiler thermal system and the operating constraints of the boiler unit.

5. The method according to claim 3, characterized in that, The turbine types include extraction-condensing and back-pressure turbines. The process of constructing a turbine model based on the required parameters corresponding to each turbine type includes: The shaft power function of the extraction-condensing steam turbine is constructed based on the periodic extraction steam mass flow rate, the number of separable operating units of the unit, and the flow balance parameter set. The working fluid energy state constraint is defined by the process parameters. The extraction-condensing steam turbine model is constructed based on the shaft power function of the extraction-condensing steam turbine and the working fluid energy state constraint. Based on the input mass flow rate of the working fluid and the thermodynamic state parameter set, a back-pressure mechanical shaft-end output power function is constructed to obtain a back-pressure steam turbine model.

6. The method according to claim 3, characterized in that, The required constraints include: constraints for the first steam level, and the method further includes: The constraints for the first steam level are constructed based on the steam flow rate of the first steam level generated in the cycle, the steam inlet steam flow rate of the first steam level turbine, and the steam flow rate of the first-level steam entering the desuperheating and pressure reducing valve to convert it into the second steam level.

7. The method according to claim 6, characterized in that, The required constraints include constraints for the second steam level and constraints for the third steam level, and the method further includes: The constraints for the current steam level are constructed based on the following: the steam extraction flow rate of the turbine converting steam from the previous steam level to the current steam level; the steam flow rate of the current steam level exiting the desuperheating and pressure reducing valve; the steam flow rate of the current steam level generated by the energy recovery unit in the catalytic cracking unit group; the process steam sales flow rate of the current steam level; and the steam flow rate of the current steam level entering the desuperheating and pressure reducing valve to convert it into the next steam level. The current steam level includes either the second steam level or the third steam level.

8. The method according to claim 7, characterized in that, The required constraints include constraints for the fourth steam class, and the method further includes: The constraints for the fourth steam class are determined based on the steam outflow rate of the second steam class turbine, the steam outflow rate of the third steam class turbine, the steam flow rate at the desuperheating and pressure reducing valve of the fourth steam class, and the process steam sales flow rate of the fourth steam class.

9. The method according to claim 1, characterized in that, The objective function for establishing the energy-mass coupling system based on the component model of the catalytic cracking process, the component model of the steam system, and the required constraints includes: Based on the component model of the catalytic cracking process, the raw material purchase cost of each unit in the catalytic cracking unit group and the environmental treatment cost during the production process of the catalytic cracking unit group are determined, and the total cost of the catalytic cracking process is obtained. Based on the component model of the steam system and the required constraints, determine the boiler fuel consumption and purchased water supply costs, pressure reducing valve process water consumption costs, motor equipment energy consumption costs, and environmental treatment costs of the boiler during the production process, and obtain the total cost of the steam system. The benefit information of the energy-mass coupling system is determined based on the component model of the catalytic cracking process and the component model of the steam system. Based on the revenue information of the energy-mass coupling system, the total cost of the catalytic cracking process, and the total cost of the steam system, an objective function is established to minimize the total operating cost.

10. The method according to claim 1, characterized in that, The optimization algorithm based on fusion branching and reduction optimizes the multi-cycle energy-mass coupling model, including: The multi-cycle energy-mass coupling model is integrated with the catalytic cracking unit group and steam system of an actual refinery as the empirical object. Global optimization calculations are performed based on the fusion branch and reduction optimization algorithm, and the parameters of the obtained optimal solution are combined to adjust the corresponding parameters in the multi-cycle energy-mass coupling model.

11. A multi-period optimization device for an energy-mass coupling system, characterized in that, The device includes: One or more processors; and a memory storing computer-readable instructions that, when executed, cause the processor to perform the operations of the method as described in any one of claims 1 to 10.

12. A computer-readable medium having stored thereon computer instructions that can be executed by a processor to implement the method as claimed in any one of claims 1 to 10.

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