Optimization method of industrial steam-green methanol collaborative production system, equipment, medium and product

Abstract

The invention relates to the technical field of industrial control, in particular to an optimization method of an industrial steam-green methanol collaborative production system, equipment, a medium and a product. According to the method, an interval parameter optimization model is constructed according to a cost objective function of the industrial steam-green methanol collaborative production system, and a stochastic optimization model is constructed according to uncertainty factors of the industrial steam-green methanol collaborative production system, so that the cost of the industrial steam-green methanol collaborative production system is optimized through a preset interactive upper and lower bound solving strategy. Solving the interval parameter optimization model and the stochastic optimization model to determine target system parameters of the industrial steam-green methanol collaborative production system, and optimizing the industrial steam-green methanol collaborative production system through the target system parameters to reduce carbon emission and improve steam methanol production efficiency.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of industrial control, in particular to an optimization method, device, medium and product of an industrial steam-green methanol collaborative production system. BACKGROUND

[0002] Industrial steam system (ISS) is a core platform for energy conversion and multi-energy collaborative utilization in industrial production, which supports continuous operation of industry and is also one of the main sources of carbon emissions.

[0003] Traditional systems generally have problems of high energy consumption, high carbon emission and high operation cost, and are difficult to meet the current requirements of green and low-carbon development. With the continuous decline in the cost of renewable energy such as wind and solar energy, it has become an urgent need to build a low-carbon, efficient and clean industrial steam system.

[0004] However, the traditional ISS mainly relies on industrial boilers burning fossil fuels to generate steam, and multi-stage extraction is performed through extraction steam turbines to drive on-site motor operation. In order to meet the needs of different process units for steam of different grades, the system usually uses pressure reducing valves for steam pressure regulation and distribution. However, such traditional energy systems have the problems of high operation cost and high carbon emission intensity, and have been difficult to meet the emission reduction requirements of the current industrial green transformation.

[0005] Therefore, an optimization method of an industrial steam-green methanol collaborative production system is needed to reduce the carbon emission of the system and improve the production efficiency of steam methanol. SUMMARY

[0006] The application provides an optimization method of a steam methanol production optimization system to reduce carbon emissions and improve system production efficiency.

[0007] In a first aspect, the application provides an optimization method of an industrial steam-green methanol collaborative production system, the method comprising:

[0008] Based on a cost objective function of the industrial steam-green methanol collaborative production system, an interval parameter optimization model is constructed; the cost objective function includes equipment cost, operation cost and carbon emission cost, and the interval parameter optimization model is used to convert steam load fluctuation into interval variables to process the uncertainty of steam demand;

[0009] Based on the uncertainty factors of the industrial steam-green methanol collaborative production system, a stochastic optimization model is constructed; the stochastic optimization model is used to generate a renewable energy output scenario tree based on the historical probability distribution of wind speed and solar radiation to process the random fluctuation of wind and light energy supply;

[0010] Solving the interval parameter optimization model and the random optimization model based on a preset interactive upper and lower bound solving strategy to determine the target system parameter of the industrial steam-green methanol collaborative production system, and performing optimization processing on the industrial steam-green methanol collaborative production system based on the target system parameter.

[0011] Optionally, before constructing the interval parameter optimization model based on the cost objective function of the industrial steam-green methanol collaborative production system, the method further comprises:

[0012] Determining the equipment cost parameter of the industrial steam-green methanol collaborative production system based on a preset discount rate and equipment life;

[0013] Determining the operation cost parameter of the industrial steam-green methanol collaborative production system based on a preset unit price coefficient;

[0014] Determining the carbon emission cost parameter of the industrial steam-green methanol collaborative production system based on a carbon tax unit price and total carbon emissions;

[0015] Determining the cost objective function based on the equipment cost parameter, the operation cost parameter and the carbon emission cost parameter.

[0016] Optionally, constructing the random optimization model based on the uncertainty factors of the industrial steam-green methanol collaborative production system comprises:

[0017] Constructing the random optimization model based on the joint probability distribution of the historical wind speed time series and the historical solar radiation intensity data.

[0018] Optionally, solving the interval parameter optimization model and the random optimization model based on a preset interactive upper and lower bound solving strategy comprises:

[0019] Constructing a lower bound sub-model based on a lower limit value of the steam demand interval to determine a first equipment configuration parameter and a first operation parameter;

[0020] Based on the first equipment configuration parameter, obtaining an upper limit value of the steam demand interval, and constructing an upper bound sub-model to solve a second operation parameter;

[0021] Determining the target operation parameter of the industrial steam-green methanol collaborative production system based on the first operation parameter and the second operation parameter.

[0022] Optionally, the target system parameter comprises a target equipment parameter and a target operation parameter, the target equipment parameter comprises the number of wind turbines, the area of solar heat collectors and the rated power of electrolytic cells, and the target operation parameter comprises a feasible solution domain of steam flow, power distribution and methanol production.

[0023] In a second aspect, the present application provides an industrial steam-green methanol co-production system, which comprises:

[0024] a steam energy module comprising an industrial boiler, a steam turbine and a pressure reducing valve, for generating and distributing steam;

[0025] a renewable energy module comprising a wind turbine and a solar collector, for converting wind energy into electricity and solar energy into low-pressure steam;

[0026] a methanol production module comprising an electrolytic cell and a methanol synthesis reactor, the electrolytic cell is used for electrolytic hydrogen production by electricity, and the methanol synthesis reactor is used for methanol production by reacting carbon dioxide in industrial flue gas with hydrogen.

[0027] Optionally, the industrial boiler is used for converting chemical energy of fuel into heat energy, generating high-temperature and high-pressure steam and outputting the steam to the steam turbine;

[0028] the steam turbine is used for steam expansion based on the steam of the industrial boiler, outputting mechanical energy and distributing steam to the pressure reducing valve;

[0029] the pressure reducing valve is used for steam pressure reduction based on preset inlet and outlet pressure thresholds, and outputs pressure-reduced steam meeting the preset pressure condition to a steam pipe network.

[0030] Optionally, the wind turbine is used for wind power generation, and the generated electricity is output to the electrolytic cell;

[0031] the solar collector is used for heat energy conversion of solar energy, and the generated steam is output to a steam pipe network;

[0032] the electrolytic cell is used for water electrolysis reaction based on the electricity of the wind turbine, and outputs hydrogen to the methanol synthesis reactor;

[0033] the methanol synthesis reactor is used for catalytic synthesis reaction based on hydrogen of the electrolytic cell and carbon dioxide in industrial flue gas, and generates the methanol.

[0034] In a third aspect, the present application provides a computer device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to realize the optimization method of any one of the industrial steam-green methanol co-production systems in the first aspect.

[0035] In a fourth aspect, the present application provides a computer storage medium, wherein the computer readable storage medium stores computer program instructions, and the computer program instructions are executed by a processor to implement the optimization method of the industrial steam-green methanol collaborative production system according to any one of the first aspect.

[0036] In a fifth aspect, the present application provides a computer program product, comprising computer program instructions, and the computer program instructions are executed by a processor to implement the optimization method of the industrial steam-green methanol collaborative production system according to any one of the first aspect.

[0037] The present application has the following advantages:

[0038] The embodiments of the present application provide an optimization method, device, medium and product of an industrial steam-green methanol collaborative production system. The method constructs an interval parameter optimization model according to a cost objective function of the industrial steam-green methanol collaborative production system, constructs a random optimization model according to uncertain factors of the industrial steam-green methanol collaborative production system, solves the interval parameter optimization model and the random optimization model through a preset interactive upper and lower bound solving strategy, determines target system parameters of the industrial steam-green methanol collaborative production system, and optimizes the industrial steam-green methanol collaborative production system through the target system parameters, so as to reduce carbon emissions and improve steam methanol production efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the drawings needed to be used in the embodiments or the related art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of the provided drawings.

[0040] Figure 1 A system architecture schematic diagram of an industrial steam-green methanol collaborative production system is provided for the embodiments of the present application;

[0041] Figure 2 An operation schematic diagram of an industrial steam-green methanol collaborative production system is provided for the embodiments of the present application;

[0042] Figure 3 A schematic diagram of operation variables of each module in an industrial steam-green methanol collaborative production system is provided for the embodiments of the present application;

[0043] Figure 4 An optimization method flowchart of an industrial steam-green methanol collaborative production system is provided for the embodiments of the present application;

[0044] Figure 5 A schematic diagram of an interval parameter optimization result of a boiler flow provided by an embodiment of the present application;

[0045] Figure 6 A schematic diagram of a multi-cycle interval parameter optimization result of a plurality of extraction steam turbines provided by an embodiment of the present application;

[0046] Figure 7 A schematic diagram of a multi-cycle interval parameter optimization result of a plurality of extraction steam turbines provided by another embodiment of the present application;

[0047] Figure 8 A schematic diagram of a multi-cycle interval optimization result of a pressure reducing valve provided by an embodiment of the present application;

[0048] Figure 9 A schematic diagram of an industrial steam system reconstruction and optimization application case provided by an embodiment of the present application;

[0049] Figure 10 A comparison schematic diagram of total cost and carbon emission of different application cases provided by an embodiment of the present application;

[0050] Figure 11 A schematic diagram of optimization results of different optimization models provided by an embodiment of the present application;

[0051] Figure 12 A schematic diagram of a structure of a computer device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0052] In order to make the objectives, technical solutions and advantages of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application. The embodiments in the present application and the features in the embodiments can be combined with each other in a non-conflicting manner. Moreover, although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown herein.

[0053] The terms "first" and "second" in the specification and claims of this application and the above drawings are used to distinguish different objects, and are not used to describe a specific order. In addition, the term "comprising" and any variations thereof are intended to cover non-exclusive protection. For example, a process, method, system, product or device including a series of steps or units is not limited to the listed steps or units, but can optionally include steps or units not listed, or can optionally include other steps or units inherent to the process, method, product or device. "Multiple" in this application can mean at least two, for example, can be two, three or more, and the embodiments of this application are not limited.

[0054] The term "and / or" in the embodiments of this application is only used to describe the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B can mean that A exists alone, A and B exist together, and B exists alone. In addition, the character " / " in this paper generally represents that the associated objects before and after are a "or" relationship.

[0055] It can be understood that in the following specific embodiments of the application, data related to industrial production is involved, and when the embodiments of the application are applied to specific products or technologies, relevant permissions or consents are required, and the collection, use and processing of relevant data need to comply with relevant laws, regulations and standards of relevant countries and regions. For example, relevant volunteers can be recruited and sign the relevant agreement of volunteer authorized data, and then the data of these volunteers can be used for implementation; or by implementing within the scope of an organization that has been authorized to allow, by using the data of the members of the organization to implement the following embodiments for data management; or the relevant data used in the specific implementation are all simulation data, for example, simulation data generated in a virtual scene.

[0056] The design idea of the embodiments of the application will be briefly introduced as follows:

[0057] In recent years, global energy consumption has surged and is expected to continue to rise. In the past 40 years, hydrocarbons (HC) have dominated global energy consumption, with their use increasing by 185% between 1970 and 2012. Considering environmental degradation and energy shortages, industrial energy saving and emission reduction is a key measure to address these challenges. As the third largest emitter of greenhouse gases, the chemical industry is also the largest consumer of fossil fuels in the industrial sector, and is a key factor in the above problems.

[0058] As the core platform of energy conversion and multi-energy collaborative utilization in industrial production, industrial steam system (ISS) is one of the main sources of carbon emissions while supporting the continuous operation of industry. The traditional system generally has problems such as high energy consumption, high carbon emission and high operation cost, which is difficult to meet the current green and low-carbon development requirements. With the continuous decline in the cost of renewable energy such as wind and solar energy, it has become an urgent need to build a low-carbon, efficient and clean industrial steam system. Moreover, the traditional ISS mainly relies on industrial boilers burning fossil fuels to generate steam, and through multi-stage extraction by extraction steam turbines to drive on-site motors. To meet the demand of different process units for each grade of steam, the system usually uses pressure reducing valves for steam pressure regulation and distribution. However, this kind of traditional energy system has the problems of high operation cost and high carbon emission intensity, which has been difficult to meet the emission reduction requirements under the current industrial green transformation and "double carbon" target.

[0059] In view of the above problems, the embodiments of the present application provide a steam methanol production optimization method, an optimization method, equipment, medium and product of an industrial steam-green methanol collaborative production system. The method constructs an interval parameter optimization model according to the cost objective function of the industrial steam-green methanol collaborative production system, and constructs a random optimization model according to the uncertainty factors of the industrial steam-green methanol collaborative production system, so as to solve the interval parameter optimization model and the random optimization model through a preset interactive upper and lower bound solving strategy, to determine the target system parameters of the industrial steam-green methanol collaborative production system, and to optimize the industrial steam-green methanol collaborative production system through the target system parameters, so as to reduce the carbon emission and improve the steam methanol production efficiency

[0060] Further, in order to meet the current green and low-carbon development requirements, the embodiments of the present application also provide a low-carbon, efficient and clean industrial steam system, i.e. an industrial steam-green methanol collaborative production system, thereby providing a theoretical basis and decision support for realizing the low-carbonization and high adaptability operation of the industrial steam system. The embodiments of the present application introduce wind power, solar energy, electrolytic water hydrogen production and green methanol process, construct an industrial steam-green methanol collaborative production system under renewable energy penetration (Renewable Energy-Integrated Steam-Methanol Production system, RE-ISMP), form a system structure of energy-matter-carbon linkage, and use an interactive upper and lower bound solving algorithm and a scenario tree generation method to solve the interval parameter optimization model and the two-stage random optimization model under the condition of considering steam load fluctuation, wind speed and solar radiation and other multi-source uncertainties, thereby providing a theoretical basis and decision support for realizing the low-carbonization and high adaptability operation of the industrial steam system.

[0061] The method and system provided by the exemplary embodiments of the present application will be described below with reference to the accompanying drawings. It should be noted that the above-mentioned application scenarios are only shown for the purpose of facilitating the understanding of the spirit and principles of the present application, and the embodiments of the present application are not limited in this respect.

[0062] Firstly, the embodiments of the present application provide an industrial steam-green methanol co-production system, which comprises:

[0063] A steam energy module, comprising an industrial boiler, a steam turbine and a pressure reducing valve, is used to generate and distribute steam.

[0064] A renewable energy module, comprising a wind turbine and a solar collector, is used to convert wind energy into electricity and convert solar energy into low-pressure steam.

[0065] A methanol production module, comprising an electrolytic cell and a methanol synthesis reactor, is used to generate hydrogen through electrolysis by electricity in the electrolytic cell, and to generate methanol by reacting carbon dioxide in industrial flue gas with the hydrogen in the methanol synthesis reactor.

[0066] Specifically, referring to Figure 1 Fig. 1 shows a system architecture schematic diagram of an industrial steam-green methanol co-production system provided by the embodiments of the present application, which Figure 1 demonstrates the architecture of the co-optimization of the industrial steam system and the methanol production system under the penetration of renewable energy, wherein the industrial steam-green methanol co-production system can optimize energy conversion and distribution by integrating renewable energy such as solar energy and wind energy, so as to improve the economic and environmental benefits of the system. Moreover, Figure 1 detailed description of the key components in the system and their interconnection mode, including a fuel oil boiler (BO), an extraction turbine (ET), a motor (MS), a pressure reducing valve (RV) and an industrial flue gas capture device. The fuel oil boiler generates high-temperature and high-pressure steam, the extraction turbine and the motor drive the system equipment, and the pressure reducing valve adjusts the steam pressure. The carbon dioxide in the industrial flue gas is captured and used for methanol synthesis. The methanol production system is composed of an electrolytic cell, a methanol synthesis device and a wind turbine, and the electrolytic cell generates hydrogen, which reacts with the captured carbon dioxide to generate methanol. The solar collector provides heat energy, and the wind turbine generates electricity to assist the system operation. Figure 1 The energy flow is also indicated in Fig. 1, including solar energy (SS), industrial steam (IS), methanol (MS), fuel oil (LS), carbon dioxide (CO2), hydrogen (H2), methanol (CH3OH) and water (H2O), which clearly demonstrates the co-working mechanism of the traditional industrial energy system and the renewable energy.

[0067] In a possible implementation, the industrial boiler in the embodiments of the present application is used to convert chemical energy of fuel into heat energy, generate high-temperature and high-pressure steam, and output to a steam turbine. The steam turbine is used to perform steam expansion based on the steam of the industrial boiler, output mechanical energy, and distribute steam to a pressure reducing valve. The pressure reducing valve is used to perform steam pressure reduction operation based on preset inlet and outlet pressure thresholds, and output pressure-reduced steam meeting the preset pressure condition to a steam pipe network.

[0068] Specifically, the industrial boiler (BO) in the present application is the core equipment of the steam energy module, and its main function is to convert chemical energy of fuel into heat energy, thereby generating high-temperature and high-pressure steam to meet the heat load demand in the industrial production process.

[0069] In a possible implementation, since different types of boilers have significant differences in steam supply mode, energy efficiency level and environmental impact, and the commonly used boiler types in the industry at present include coal-fired boilers, natural gas boilers and oil-fired boilers, among which oil-fired boilers are still widely used in many industries due to their high fuel energy density and stable combustion process, especially suitable for areas where natural gas supply is limited or specific industrial scenarios. Therefore, considering that the performance parameters (such as thermal efficiency, operating load and steam production capacity) of the boiler have an important influence on system optimization in the process of modeling the steam system, the oil-fired boiler is selected as the modeling object in the present application, which generates superheated steam (SS) by burning light oil or heavy oil and other fuels.

[0070] Specifically, the calculation formula of the boiler efficiency in the present application is as follows:

[0071]

[0072] Among them, represents the efficiency of the industrial boiler;

[0073] and is a parameter fitted by a large amount of historical data of the industrial boiler, and the working efficiency of the boiler is also related to the maximum flow rate of the boiler outlet .

[0074] The balance formula of the fuel flow rate and heat of the boiler is as follows:

[0075]

[0076] Among them, represents the flow rate of SS generated at the outlet of the BO, represents the flow rate of fuel used for combustion of the BO.

[0077] H ss , H cwrespectively, LHV represents the heat value of fuel, and the production of super-high pressure steam needs to be guaranteed within a predetermined range in industrial production:

[0078]

[0079] wherein, respectively, represent the upper and lower limits of the BO outlet SS flow.

[0080] In a possible implementation, the steam turbine in the application is used as a key heat energy conversion device in the steam energy module, which undertakes the functions of regulating and supplying multi-stage steam and is a core link between the high-temperature heat source and the terminal load.

[0081] Specifically, to meet the flexible demand for steam of different pressure grades in industrial processes, the steam turbine is generally divided into two categories: extraction steam turbine (ET) and back-pressure steam turbine (BT). Among them, ET usually adopts a multi-stage extraction structure, which is divided into multiple expansion stages according to the steam pressure gradient, and the performance is optimized in combination with isentropic efficiency. By setting extraction ports at different stages, accurate control and flexible allocation of multi-pressure grade steam can be realized to improve the heat energy utilization efficiency of the system. Therefore, to accurately describe the operation behavior of ET, the application examples will model it based on a large amount of historical operation data, so as to determine its key thermodynamic parameters.

[0082] Specifically, the working process of ET in the application can be described by enthalpy change, wherein H et,in , H et,ext , H et,exh respectively represent the inlet enthalpy, extraction enthalpy and exhaust enthalpy of ET, and the thermodynamic characteristics and energy conversion process can be shown as follows:

[0083]

[0084] wherein, represents the flow rate of extraction, represents the flow rate of exhaust, respectively represent the minimum flow rate and maximum flow rate of the inlet flow rate of ET, as shown below:

[0085]

[0086] Further, the working principle of the back-pressure steam turbine is shown in the following formula:

[0087]

[0088] wherein, Hbt,in , H bt,out respectively represent the inlet enthalpy and the outlet enthalpy of the back pressure turbine; respectively represent the shaft power generated by the BT, and the BT inlet steam flow.

[0089] In a possible implementation, the motor serves as a driving unit in the steam energy module, mainly undertakes the mechanical energy conversion task, and is a key component for realizing continuous and stable operation of the process equipment.

[0090] Specifically, to improve the overall energy efficiency level and operation economy of the system, a motor and BT collaborative driving strategy is usually adopted to realize optimal scheduling and efficient distribution of energy. In the embodiments of the present application, based on idealized assumptions, the motor does not have power loss during operation and always stably outputs at rated power. On this basis, the energy conversion process of the motor can be characterized by a mathematical model, and the power input-output characteristics and energy efficiency relationship are as follows:

[0091]

[0092] wherein the total power used by the motor is P Motor , the driving power of the steam is P bt , and σ bt is a binary integer variable. When σ bt is 1, the motor is driven by electricity; and when σ bt is 0, it means that the motor is driven by steam.

[0093] In a possible implementation, the letdown valve (LV) is a key control unit for adjusting the pressure and temperature of the steam in the steam energy module, and is widely used in multi-stage steam supply structures to meet the needs of different process units for pressure levels and to ensure the safety and stability of system operation. The LV reduces the pressure and temperature of the steam through a throttling process to meet the working conditions of the downstream equipment. However, this throttling process is an irreversible process, and the enthalpy remains unchanged before and after pressure reduction, but due to the lack of energy recovery mechanism, the effective energy of the steam is significantly lost, thereby reducing the overall energy utilization efficiency of the system. Therefore, the embodiments of the present application will optimize the design of the industrial steam system, and the use frequency and load of the LV should be reduced as much as possible.

[0094] Specifically, the energy conservation and mass conservation relationship of the LV is as follows:

[0095]

[0096] wherein the related thermodynamic parameters H lv,in , H lv,out and H lv,cwThey represent the enthalpy of the LV inlet steam, the enthalpy of the outlet steam and the enthalpy of the circulating water respectively. These parameters are fitted by the least squares method through a large amount of historical data. The main problem is that the steam throttling process cannot recover energy.

[0097] In summary, during optimization, it is necessary to reasonably constrain the use of the pressure relief valve to reduce unnecessary energy loss. Therefore, the embodiment of the present application imposes constraints on the inlet steam flow through the LV to optimize system operation, improve energy utilization efficiency, and reduce operating costs. The specific constraints are as follows:

[0098]

[0099] in, They represent the minimum and maximum flow rates of the LV inlet steam respectively, in t / h.

[0100] In one possible implementation, the wind turbine in the embodiment of the present application is used to generate wind power and output the generated electrical energy to the electrolyzer. The solar collector is used to convert solar energy into thermal energy and output the generated steam to the steam network.

[0101] In one possible implementation, wind turbines (WTs) are highly efficient renewable energy conversion devices widely used in areas with abundant wind resources. They demonstrate excellent power generation performance and potential for large-scale deployment, particularly in coastal areas and areas with high wind speeds. As a key component of green energy, WTs convert wind energy into electricity, effectively reducing reliance on fossil fuels and mitigating the carbon emissions and environmental pollution associated with traditional thermal power generation.

[0102] Specifically, in the embodiment of the present application, WT is incorporated into the green power supply structure of the renewable energy module. As the main clean energy input unit, it can not only provide partial power support for industrial loads, but also has the potential to supply power to the people's livelihood power grid, further promoting the low-carbon and sustainable transformation of the energy system.

[0103] Specifically, the power generation capacity and operating characteristics of WT can be described by a mathematical model, and the corresponding power output relationship is as follows:

[0104]

[0105] Among them, P rated Indicates the rated power of the wind turbine, v really,t , v in , v out , v rated They respectively represent the actual wind speed, cut-in wind speed, cut-out wind speed and the rated wind speed for normal operation of the fan, all in m / s.

[0106] In one possible implementation, the solar heat collector (SHC) in the embodiments of the present application is a key heat conversion device that converts solar radiation energy into heat energy, and is widely used in the field of renewable energy. In the renewable energy module of the present application, the solar heat collector is mainly used for the production of low-pressure low-temperature steam to meet the energy demand of a specific industrial process.

[0107] Specifically, the SHC converts solar radiation into heat energy by absorbing it, and the total heat absorbed depends on multiple key factors, including the surface absorption efficiency of the collector, the effective heat collection area, and the intensity of solar radiation. In order to accurately describe the heat energy conversion process of the SHC, the embodiments of the present application can use a mathematical model for quantitative analysis, and the mathematical expression is as follows:

[0108]

[0109] where S shc represents the surface area of the SHC, R solar,t (φ) represents the product of solar radiation and heat absorption efficiency at time t, U L (T in -T a represents the energy reduced due to heat loss of the SHC. The temperature difference between the fluid in the SHC T in and the ambient temperature T a affects the efficiency of heat transfer.

[0110]

[0111] The production of low-pressure steam LS needs to meet the energy balance condition shown in the above formula, where C cw represents the flow rate of low-pressure steam produced by the SHC at time t, C sat , C cw , r LS represents the specific heat capacity of water, saturated steam, and the latent heat of water, T sat , T cw represents the temperature of low-pressure steam, saturated steam, and heating water.

[0112] In one possible implementation, the electrolytic cell in the embodiments of the present application is used to perform a water electrolysis reaction based on the electrical energy of a wind turbine, and outputs hydrogen gas to a methanol synthesis reactor. The methanol synthesis reactor is used to perform a catalytic synthesis reaction based on the hydrogen gas from the electrolytic cell and carbon dioxide in industrial flue gas, to generate methanol.

[0113] In one possible implementation, the hydrogen production by electrolysis of water in the methanol production module in the embodiments of the present application is a typical clean and sustainable hydrogen production path, which has important application value in the context of energy structure transformation. Compared with traditional fossil fuel hydrogen production processes (such as natural gas steam reforming), the hydrogen production technology by electrolysis of water does not need to rely on fossil energy, and theoretically does not produce greenhouse gas emissions in the hydrogen production process, and is widely recognized as an important technical support for realizing green hydrogen production. The hydrogen (H2) generated by electrolysis of water can be widely used in fuel cells, power storage and industrial raw materials in many low-carbon fields, and provides a key foundation for building a clean energy system.

[0114] Specifically, in the methanol production module in the embodiments of the present application, hydrogen production by electrolysis of water can be realized by running an electrolyzer (ER), which generates hydrogen (H2) and byproduct oxygen (O2) by driving a water decomposition reaction through external electric energy. The electrochemical mechanism of this process is described by the following chemical reaction formula. The energy consumption level of the electrolyzer directly affects the power load distribution and hydrogen production rate of the system, and is an important parameter in the energy flow modeling and carbon emission reduction effect evaluation of the system. The specific reaction equation is as follows:

[0115]

[0116] Current hydrogen production by electrolysis of water mainly includes alkaline electrolyzer (AE) and proton exchange membrane electrolyzer (PEM). Compared with traditional AE, PEM has significant advantages in energy conversion efficiency, structural compactness and electrolysis stability, and is therefore widely regarded as a key technical path for efficient green hydrogen production. In the embodiments of the present application, a PEM electrolyzer can be selected as a hydrogen production unit to improve the energy efficiency and low-carbon operation capability of the system.

[0117] Further, to simplify the electrochemical reaction mechanism and improve the solvability of the model, a simplified mathematical model of the PEM is constructed in the present application, and its expression is as follows:

[0118]

[0119] wherein, represents the mass of hydrogen produced, in kg / h, η PTH represents the conversion efficiency of power-to-H2, ω PTH refers to the conversion factor of power-to-H2.

[0120] In one possible implementation, the methanol production in the embodiments of the present application is a sustainable chemical production path integrating carbon capture and renewable energy driving, aiming to realize efficient utilization of carbon dioxide resources and deep emission reduction of industrial systems. In the process, the carbon dioxide (CO2) discharged by the system is captured and then reacts with green hydrogen (H2) produced by electrolytic cells driven by renewable energy such as wind energy and solar energy under catalytic conditions to generate methanol (CH3OH). This process not only has significant carbon emission reduction potential, but also builds an efficient carbon capture and utilization (Carbon Capture and Utilization, CCU) path, providing technical support for the green transformation of industrial energy systems.

[0121] Specifically, the processing process of the methanol production module in the embodiments of the present application mainly includes the following three core links: (1) green hydrogen is prepared by wind power or solar power generation. (2) CO2 is recovered from industrial emission sources by using carbon capture technology. (3) CO2 and H2 are driven to occur methanol synthesis reaction under specific catalyst and reaction conditions.

[0122] In the above reaction process, carbon dioxide and hydrogen combine to produce methanol, and the chemical reaction equation is as follows:

[0123] CO2(g)+3H2(g)→CH3OH+H2O(g)

[0124] In an ideal case, the relationship between the amount of carbon dioxide and hydrogen and the methanol production is as follows:

[0125]

[0126] In one possible implementation, referring to Figure 2 FIG. 1 shows a schematic diagram of the operation of an RE-ISMP system provided by the embodiments of the present application, which comprehensively shows the collaborative optimization architecture of the industrial steam system and the methanol production system. The system realizes efficient utilization of energy and production of chemicals by integrating solar energy, wind energy and traditional energy. Figure 2 FIG. 1 shows a schematic diagram of the operation of an RE-ISMP system provided by the embodiments of the present application, which comprehensively shows the collaborative optimization architecture of the industrial steam system and the methanol production system. The system realizes efficient utilization of energy and production of chemicals by integrating solar energy, wind energy and traditional energy. Figure 2The energy flow between different devices is also indicated, including flue gas, steam, circulating water, and electricity. The steam demand (HS, MS, LS) corresponds to different levels of steam demand, respectively 192.5, 130, and 52.5 units. In addition, Figure 2 The unit conversion between the power system and the production system is also shown, with the power system unit being kWh and the steam system and production system unit being t / h. Through this architecture, the system can effectively integrate renewable energy, optimize energy conversion and distribution, and achieve economic and environmental goals of the system. Further, Figure 3 The operation variables of the material flow, energy consumption, and power output of each module in the RE-ISMP system provided by the embodiments of the present application are shown. In the model, the required power is completely supplied by wind turbines, achieving full renewable of the power system. At the same time, the system uses electric motors to completely replace traditional back-pressure steam turbines to drive key unit operations. This configuration selection is based on the current modeling results, and using wind power under existing conditions not only has high cleanliness, but also has significant economic advantages, so it becomes the optimal driving energy form in the system.

[0127] For the convenience of description, each part above is divided into unit modules (or modules) according to function and described respectively. Of course, the functions of each unit (or module) can be implemented in the same or multiple software or hardware when implementing the present application. Those skilled in the art can understand that each aspect of the present application can be implemented as a system, a method, or a program product. Therefore, each aspect of the present application can be specifically implemented in the following forms: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software aspects, which can be collectively referred to as "circuitry", "module" or "system".

[0128] The system can be used to perform the methods shown in the embodiments of the present application, and therefore the functions that can be achieved by each functional module of the system can be referred to the description of subsequent embodiments, which will not be described in more detail.

[0129] Reference Figure 4 As shown in the figure, a flowchart of an optimization method for an industrial steam-green methanol co-production system provided by the embodiments of the present application is shown. The specific implementation process of the method is as follows:

[0130] Step 401: Based on the cost objective function of the industrial steam-green methanol co-production system, an interval parameter optimization model is constructed.

[0131] In the embodiments of the present application, the cost target function integrates the equipment cost, operation cost and carbon emission cost of the system. The present application establishes a mixed integer linear programming (MILP) model with the minimum operation cost and carbon tax cost as the target through the operation mechanism of the key units of the industrial steam-green methanol co-production system such as the boiler, extraction steam turbine and electrolytic cell.

[0132] In a possible implementation, the embodiments of the present application can determine the average equipment cost of the industrial steam-green methanol co-production system through a preset discount rate and equipment life; determine the average operation cost of the industrial steam-green methanol co-production system through a preset unit price coefficient; and determine the average carbon emission cost of the industrial steam-green methanol co-production system through the carbon tax unit price and total carbon emission, so as to determine the total average cost of the industrial steam-green methanol co-production system according to the average equipment cost, average operation cost and average carbon emission cost, and determine the corresponding cost target function.

[0133] Specifically, the embodiments of the present application take minimizing the comprehensive cost of the RE-ISMP system as the optimization target, aiming to synergistically improve the economy and environmental sustainability of the system. The target function systematically integrates multiple cost elements, including boiler fuel consumption, purchased circulating water cost, motor electricity cost, investment and operation and maintenance cost of renewable energy equipment, electrolytic cell equipment cost, methanol synthesis processing cost, purchased low-pressure steam cost and carbon tax cost, etc. At the same time, economic return factors such as green methanol product sales, power surplus feedback to the power grid, and low-pressure steam sales revenue are considered, so as to maximize the overall economic benefit on the basis of guaranteeing the carbon emission reduction target.

[0134] Specifically, the total annual average cost TAC of the system in the embodiments of the present application is as follows:

[0135]

[0136] Among them, AOC, AEC and ACC represent the annual average operation cost, the annual average equipment cost and the annual average carbon emission cost, respectively.

[0137] c fuel , c water , c e , c ls , c tax , c wt , c solar , c ER , c pro represent the prices of fuel, water, electricity, purchased low-pressure steam, sold green methanol, carbon tax, wind turbine and solar collector and methanol processing, respectively.

[0138] r, y represent the discount rate and the operating year, respectively, represents the carbon emissions in the entire industrial process, as follows:

[0139]

[0140] In the industrial production process, different temperatures and pressures of steam provided by the industrial energy system are consumed to meet various production demands. Therefore, in the production process, it is necessary to ensure that each level of steam meets the energy balance of different demands. Therefore, the embodiments of the present application also provide coupling constraints between each level of model, as follows:

[0141]

[0142]

[0143] wherein, represents the demand for high-pressure steam, medium-pressure steam, and low-pressure steam at time t, respectively.

[0144] P ER ,P os , represents the amount of electricity used for electrolytic cell hydrogen production and the amount of electricity purchased from the power grid to make up for the insufficient power demand, and the maximum power of the electrolytic cell.

[0145] represents the output of hydrogen produced by electrolysis of water, and the minimum and maximum values, represents the flow rate of purchased low-pressure steam and the minimum flow rate of low-pressure steam, in t / h.

[0146] Therefore, on the basis of the objective function of minimizing the total cost of the system, the embodiments of the present application combine the coupling constraints of each unit device in the energy and material flow level to construct an industrial steam system and methanol co-production optimization model under the background of renewable energy penetration, i.e. the uncertainty optimization model in the embodiments of the present application. The model covers multiple types of variables such as continuous variables (such as energy flow, mass flow) and binary decision variables (such as equipment start-stop state, driving mode selection), has strong structural complexity, and the optimization problem of the industrial steam-green methanol co-production system is formalized as a mixed-integer linear programming (MILP) model.

[0147] Step 402: Based on the uncertainty factors of the industrial steam-green methanol co-production system, a stochastic optimization model is constructed.

[0148] In the embodiment of the present application, considering that the industrial steam-green methanol collaborative production system is affected by multiple sources of uncertainty factors such as steam load fluctuations, wind speed and solar radiation, a multi-period interval parameter optimization model and a two-stage stochastic optimization model are constructed. Among them, the interval parameter optimization model is used to convert steam load fluctuations into interval variables to deal with the seasonal uncertainty of steam demand. The stochastic optimization model is used to generate a renewable energy output scenario tree based on the historical probability distribution of wind speed and solar radiation to deal with the random fluctuations of wind and solar energy supply.

[0149] In a possible implementation, the embodiment of the present application may construct an interval parameter optimization model by combining historical steam load fluctuation data, historical wind speed time series, and historical solar radiation intensity data with a preset interval parameter planning strategy.

[0150] Specifically, the embodiments of the present application take into account the periodic changes in wind speed and solar radiation in different months, as well as the daily fluctuations in industrial steam demand. The system operating parameters need to be dynamically adjusted with external conditions, thereby presenting certain variability and uncertainty during operation. Based on the Interval Parameter Programming (IPP) theory, the key parameters related to steam demand in the REISMP system are modeled in the form of intervals, and a multi-period REISMP interval optimization model, namely, an interval parameter optimization model, is constructed. Through this interval parameter optimization model, steam load fluctuations are converted into interval variables to deal with the seasonal uncertainty of steam demand.

[0151] In one possible implementation, the stochastic optimization model in the embodiments of the present application is divided into two phases: the first phase is used to determine the configuration decision variable X1 of the renewable energy equipment, and the second phase is to optimize the operational variable X2 during the operation period, thereby achieving hierarchical optimization coordination between the equipment layer and the operation layer. The objective function of the model is to minimize the total annual cost, specifically including the annual average equipment cost AEC of the renewable energy equipment, the annual average carbon emission cost ACC, and the annual average operating cost AOC, as shown below:

[0152]

[0153] In addition, except for the investment cost, the remaining operating parameters of the system in the embodiment of the present application are modeled in the form of intervals to characterize the feasible variation range of the system operating parameters under multi-source uncertainty conditions, thereby determining the operating boundaries of the system.

[0154] Furthermore, in the embodiment of the present application, the constraints corresponding to the boiler, turbine, motor, and renewable energy of the system still follow the constraints of deterministic optimization. Only the steam balance constraint needs to be re-described as follows:

[0155]

[0156] In summary, according to the statistical wind speed and solar radiation average values in a certain time range (for example, 12 months), the interval parameter optimization problem can be described as an interval parameter MILP problem.

[0157] Step 403: Based on the preset interactive upper and lower bound solving strategy, the interval parameter optimization model and the random optimization model are solved to determine the target system parameters of the industrial steam-green methanol collaborative production system.

[0158] In the embodiments of the present application, after considering the steam load fluctuation, wind speed, solar radiation and other multi-source uncertainty factors, the interval parameter optimization model and the random optimization model in the multi-period are constructed, the interactive upper and lower bound solving algorithm and the scene tree generation method are used to solve the interval parameter optimization model and the random optimization model, so as to determine the target system parameters of the industrial steam-green methanol collaborative production system, and the industrial steam-green methanol collaborative production system is optimized based on the target system parameters.

[0159] In a possible implementation, the lower bound sub-model can be constructed based on the lower limit value of the steam demand interval of the interval parameter optimization model to determine the first equipment configuration parameter and the first operation parameter. The upper limit value of the steam demand interval is obtained by fixing the first equipment configuration parameter, and the upper bound sub-model is constructed to solve the second operation parameter. The first operation parameter and the second operation parameter are combined to determine the feasible solution domain of the target operation parameter of the industrial steam-green methanol collaborative production system.

[0160] Specifically, the interval parameter optimization problem is described as an interval parameter MILP problem, and for the MILP problem, the branch and cut algorithm is used to obtain the global optimal solution of the current optimization problem. For the IPP problem, the interactive algorithm is used to solve the interval parameter programming problem. The IPP can be described as follows:

[0161] min F ± = A ± X ±

[0162] s.t. A ± X ± ≤ B ±

[0163] X ± ≥ 0

[0164] The interactive algorithm is divided into three steps. The first step is to divide the model into two sub-models according to the variables and constraints of the original problem, and the two sub-models correspond to the lower bound and upper bound of the original problem. Then the corresponding solution is solved according to the two sub-models. The third step is to integrate the solutions of the two sub-models into the form of an interval to obtain the optimal solution of the original model. The RE-ISMP system designed in combination with the present application divides the optimization problem into two sub-models. In actual problems, the introduction of general equipment must meet the demand of the lower limit, so the lower bound model of the RE-ISMP interval optimization problem is expressed as:

[0165] minTAC = minAEC + AOC + ACC

[0166]

[0167] The steam demand in the constraint is set as the minimum value of the interval, so the constraint can be expressed as:

[0168]

[0169] The optimal solution is obtained using the Gams solver The renewable energy equipment solution in the optimal solution calculated by the lower bound is fixed, and the steam demand is adjusted to the maximum value of the interval, that is, the upper bound sub-model, which can be described as:

[0170]

[0171] The constraint is:

[0172]

[0173] Wherein, The optimal solution can be obtained by solving the value calculated by the lower bound sub-model. In combination with the lower bound sub-model, the upper bound sub-model, the optimal solution of the original IPP model is:

[0174]

[0175] TAC ± = [TAC - , TAC + ]

[0176] In one possible implementation, the present application embodiment provides an IPP interactive solving process, as shown below:

[0177] Establishing an IPP model of the RE-ISMP system;

[0178] Based on the interactive solution, the IPP model of the RE-ISMP system is decomposed into a lower bound sub-model and an upper bound sub-model, where the lower bound sub-model corresponds to the minimum annual total cost TAC finally solved. - , the upper bound sub-model corresponds to the maximum annual total cost TAC + ;

[0179] Establish the lower bound sub-model;

[0180] Solve the lower bound sub-model to obtain the renewable energy equipment variables The solution of and the solution of the operating variables of the RE-ISMP system and the minimum annual total cost TAC - ;

[0181] Establish the upper bound submodel;

[0182] Renewable energy equipment variables The solution of is brought into the lower bound submodel to obtain the solution of the operating variables of the RE-ISMP system and the maximum annual total cost TAC + ;

[0183] Merge the solutions of the two submodels.

[0184] In one possible implementation, reference Figure 5 The figure shows a schematic diagram of the interval parameter optimization results of a boiler flow provided by an embodiment of the present application. After the interval parameter optimization, the steam flow of boiler 1 remains unchanged, and its inlet flow in each month of the year is stable at 35.02t / h, and the outlet flow is maintained at 170t / h, indicating that it is a base load device in the system, with a stable operating state and does not participate in responding to uncertainty disturbances. In contrast, boilers 2 and 3 assume the role of regulating interval parameter disturbances in the system. Among them, boiler 2 is mainly responsible for fine-tuning during the winter (January-February and November-December) to adapt to slight fluctuations in the load; and boiler 3, as the main regulating device, has a larger fluctuation range in its inlet and outlet steam flows throughout the year, reflecting its greater flexibility and adjustment capabilities in uncertain scenarios. Through this division of labor and cooperation, the system ensures the effective response of the optimization model to changes in interval parameters while achieving operational robustness.

[0185] In one possible implementation, reference Figure 6 and Figure 7The results of multi-period interval parameter optimization of four extraction steam turbines are respectively shown, and different ET units bear different roles in dealing with uncertain disturbances. The upper limit flow of ET1 fluctuates greatly in the whole year, showing a U-shaped trend, maintaining at a high level in winter (November-December, January-February), and decreasing to about 227 t / h in summer, so ET1 bears the main role of adjusting in the load peak period in the system. The lower limit flow remains stable throughout the year, always maintaining at 221.5 t / h, indicating that ET1 bears the base load role of stable operation in the system. ET2 shows strong seasonal regulation capacity. Its upper limit flow basically maintains at 200 t / h throughout the year, but the lower limit flow decreases to the lowest (about 177 t / h) in May-July, and rises to 195 t / h in winter, indicating that the device adjusts the minimum operating load to cope with the seasonal fluctuations of uncertainty. ET3 has relatively small operation fluctuation, and ET4 shows similar characteristics to ET2, and the upper and lower limits of the overall flow decrease obviously in summer. In summary, ET1 and ET2 mainly bear the main operating load throughout the year, representing the base load support and main regulation capacity of the system, while ET3 and ET4 play an auxiliary regulation role, together ensuring the stability and economy of the system under the uncertainty of interval parameters.

[0186] In a possible implementation, reference is made to Figure 8 The results of multi-period interval optimization of a pressure reducing valve provided by the embodiment of the application are shown. The pressure reducing valve mainly adjusts the steam demand, so the upper and lower limits of LV3 may sometimes show opposite trends, and LV1 and LV2 basically maintain the state of not being used if the steam demand adjustment needs to adjust the pressure reducing valve to control production.

[0187] Step 404: performing optimization processing on the industrial steam-green methanol collaborative production system based on the target system parameters.

[0188] In the embodiment of the application, the target system parameters can include target device parameters and target operation parameters of the industrial steam-green methanol collaborative production system. For example, the target device parameters can include the number of wind turbines, the area of solar heat collectors and the rated power of electrolytic cells, and the target operation parameters can include the feasible solution domain of steam flow, power distribution and methanol production.

[0189] Specifically, the embodiment of the present application can determine the lower limit of boiler fuel supply through the preset minimum extraction flow of the extraction steam turbine; distribute wind power generation through the minimum starting power of the electrolyzer; limit the medium-pressure steam pressure reduction through the maximum opening threshold of the pressure reducing valve; adjust the CO2 capture rate according to the maximum processing capacity of the methanol synthesis reactor; control the steam turbine to perform multi-stage extraction flow distribution, the pressure reducing valve to perform steam pressure reduction operation, and the electrolyzer to perform hydrogen production power distribution, etc., through the operating parameter range obtained by solving.

[0190] In one possible implementation, the embodiments of the present application provide three types of typical optimization frameworks for evaluating the synergistic benefits and system economics under different strategy combinations. First, a base model (BM) is constructed based on the traditional industrial steam system as a reference. Secondly, the optimization model of the industrial steam system after the access of renewable energy (Renewable Energy Integrated Steam System, RE-ISS) is considered to analyze the impact of green energy penetration on the system structure. Finally, a synergistic optimization model of the industrial steam system and the green methanol production system under the penetration of renewable energy (Renewable Energy Integrated Steam–Methanol Production, REISMP) is further constructed to comprehensively evaluate the system's multi-energy complementarity and carbon emission reduction potential.

[0191] Specifically, refer to Figure 9 Shown are schematic diagrams of three application cases of industrial steam system transformation and optimization provided in the embodiments of the present application, namely, a traditional industrial steam system (Case I), a renewable energy industrial steam system (Case II), and a coupling system of industrial steam system and methanol production under renewable energy penetration (Case III). In Case I, the system mainly relies on boilers to generate steam and uses steam turbines for energy conversion, and does not involve renewable energy. Case II introduces solar energy and wind energy, where solar energy is used to heat boilers and wind energy drives steam turbines, thereby improving energy efficiency. Case III further couples methanol production, where solar energy and wind energy are not only used to generate steam, but also to electrolyze water to produce hydrogen, which reacts with captured carbon dioxide to generate methanol, thereby achieving efficient integration of energy and chemical production. In this way, the above three cases describe in detail the gradual optimization process of industrial steam systems from traditional to renewable energy penetration, and how to improve the overall benefits of the system by integrating renewable energy and chemical production processes.

[0192] For further reference, Figure 10A comparison diagram of total cost and carbon emission of different application cases provided by the embodiment of the application, and reference is made to Figure 11 A diagram of optimization results of different optimization models provided by the embodiment of the application, and reference is made to Figure 11 The optimization results of the RE-ISMP system constructed by the embodiment of the application and the comparison results with the BM system and the RE-ISS system of the comparative group are shown in the table. Obviously, compared with the traditional BM system in Case I, the industrial steam system RE-ISS in Case II with renewable energy introduced has a total cost reduction of 4.67% and a carbon emission reduction of 6.36%. In Case I, the system operation cost accounts for 87.8% of the total cost, and the carbon tax cost accounts for 12.2%. In Case II, the operation cost ratio is reduced to 85.8%, the carbon tax cost is 12.0%, and the new investment cost accounts for 2.7%. In the RE-ISS system, 14 wind turbines are introduced to replace industrial electricity, and 344,168 m 2 of solar collectors are configured to meet the low-pressure steam demand, and the system does not set an electrolytic cell and completely relies on the solar collector to provide heat. Case III is the RE-ISMP system designed by the application, which integrates green methanol production. Compared with Case II, the system converts CO2 in industrial tail gas and renewable energy generated electricity into green methanol products with economic value, realizing further economic and environmental benefit improvement. Specifically, the RE-ISMP system further reduces the total cost by 0.02% and reduces carbon emissions by 2.32% compared with Case II. To meet the energy demand of renewable energy power generation for hydrogen production and methanol synthesis, the system adds 75 wind turbines, the area of the solar collector remains unchanged, and the maximum electrolysis power of the electrolytic cell is set to 33,108 KW. In terms of cost structure, the operation cost ratio in Case III is 75.6%, the carbon tax cost is 11.8%, and the investment cost is significantly increased to 12.6%. The results show that the RE-ISMP system realizes further reduction of carbon emissions in the operation stage and efficient use of resources by increasing the initial investment, and has good economic and environmental synergistic benefits.

[0193] Specifically, the embodiment of the present application can use a typical refinery steam system as a benchmark model BM, and verify the effectiveness of the RE-ISMP system optimization model through comparative analysis. Numerical modeling is implemented in a general algebraic modeling system (e.g., GAMS v33.1.0), and is solved by using a branch-and-bound global optimizer BARON. The solver is based on interval analysis and convex relaxation techniques, and has strict global convergence in mixed-integer nonlinear programming problems, and is particularly suitable for non-convex energy system optimization problems. The calculation experiment can be completed on a computing node equipped with an Intel Core i9-14900HX processor, and the maximum memory occupation is 8.3 GB per single iteration.

[0194] Referring to Figure 12 Based on the same technical concept, the embodiment of the present application also provides a computer device 120. In an embodiment, the computer device can be a device dedicated to optimizing an industrial steam-green methanol co-production system, or can be a control device for overall control of a steam-methanol production process. The computer device is as shown in Figure 12 As shown in the figure, the computer device 120 comprises a memory 1201, a communication module 1203, and one or more processors 1202.

[0195] The memory 1201 is used to store computer programs executed by the processor 1202. The memory 1201 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system and programs required for running instant messaging functions, etc.; and the data storage area can store various instant messaging information and operation instruction sets, etc.

[0196] The memory 1201 can be a volatile memory (volatile memory), such as a random access memory (random-access memory, RAM); the memory 1201 can also be a non-volatile memory (non-volatile memory), such as a read-only memory, a flash memory, a hard disk drive (hard disk drive, HDD) or a solid-state drive (solid-state drive, SSD); or the memory 1201 is any other medium capable of carrying or storing desired program codes in the form of instructions or data structures and capable of being accessed by a computer, but is not limited to this. The memory 1201 can be a combination of the above-mentioned memories.

[0197] The processor 1202 can include one or more central processing units (central processing unit, CPU) or digital processing units, etc. The processor 1202 is used to invoke the computer programs stored in the memory 1201 to implement the above-mentioned optimization method of the industrial steam-green methanol co-production system.

[0198] The communication module 1203 is configured to communicate with the industrial control system.

[0199] The specific connection medium between the memory 1201, the communication module 1203 and the processor 1202 is not limited in the embodiments of the present application. In the embodiments of the present application, the memory 1201 and the processor 1202 are connected through the bus 1204, and the bus 1204 is described by a thick line in the embodiments of the present application. The connection mode between other components is only schematically described, and is not limited. The bus 1204 can be divided into an address bus, a data bus, a control bus and the like. For the convenience of description, only one thick line is used to describe the bus 1204 in the embodiments of the present application, but it is not described that there is only one bus or only one type of bus. Figure 12 Figure 12 Figure 12

[0200] The memory 1201 stores a computer storage medium, and the computer storage medium stores computer executable instructions. The computer executable instructions are used to implement the optimization method of the industrial steam-green methanol collaborative production system according to the embodiments of the present application. The processor 1202 is configured to execute the optimization method of the industrial steam-green methanol collaborative production system according to the embodiments of the present application.

[0201] Based on the same inventive concept, the embodiments of the present application also provide a storage medium storing a computer program, which, when executed on a computer, causes the computer to perform the steps of the optimization method of the industrial steam-green methanol collaborative production system according to the embodiments of the present application described in the specification.

[0202] In some possible implementation manners, each aspect of the optimization method of the industrial steam-green methanol collaborative production system provided by the present application can also be implemented in the form of a computer program product, which includes a computer program. When the program product is executed on a computer device, the computer program is used to cause the computer device to execute the steps of the optimization method of the industrial steam-green methanol collaborative production system according to the embodiments of the present application described in the specification, for example, the computer device can execute the steps of the embodiments.

[0203] ​​​The program product of the embodiments of the present application can employ any combination of one or more computer-readable media. The computer-readable media can be a computer- readable storage medium or a computer-readable signal medium. The computer-readable storage medium can be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium include an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0204] The program product of the embodiments of the present application can employ a portable compact disc read-only memory (CD-ROM) and include a computer program, and can be run on a computer device. However, the program product of the present application is not limited thereto, and in the present application, the computer-readable storage medium can be any tangible medium containing or storing a program, which includes a computer program that can be used by or in conjunction with a command execution system, apparatus, or device.

[0205] The computer-readable signal medium can include a data signal traveling in a baseband or a carrier wave traveling in a baseband, in which the computer-readable program is contained. Such a traveling data signal can take on many forms, including but not limited to electro-magnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium that is not a storage medium and that can be used to carry or transmit a program for use by or in connection with a command execution system, apparatus, or device.

[0206] The computer program contained in the computer-readable medium can be transmitted in any suitable medium, including but not limited to wireless, wired, optical fiber, RF, and the like, or any suitable combination thereof.

[0207] The computer program for performing the operations of the present application can be written in any combination of one or more programming languages, including an object-oriented programming language such as Java, C++, and the like, and a conventional procedural programming language such as the "C" programming language or a similar programming language.

[0208] It should be noted that although several units or sub-units of the apparatus are mentioned in the above detailed description, such a division is merely exemplary and not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided into multiple units.

[0209] Moreover, although the operations of the method(s) herein can be described in a particular, sequential order, this is not intended to be a requirement or a limitation. Rather, additional steps can be provided before, after, or in between the described steps, and the method(s) can be implemented without some or all of the described steps. Further, steps can be executed in an order other than that described.

[0210] Those skilled in the art will appreciate that embodiments of the present application can be devised for a method, a system, or a computer program product. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer readable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer readable program code.

[0211] While the preferred embodiments of the application have been described, additional variations and modifications can be employed by those skilled in the art without departing from the spirit and scope of the application. Therefore, it should be understood that the appended claims are intended to cover all such modifications and variations as falling within the scope of the application. Accordingly, the application is intended to embrace all such alterations, modifications, and variations that fall within the scope of the appended claims. In addition, while a particular feature of the application can have been disclosed with respect to only one of several embodiments, such feature can be combined with one or more other features of the same or different embodiments as can be desired and advantageous for any given or

[0212] Obviously, numerous modifications and variations of the present application are possible in light of the above teachings. It is therefore to be understood that within the scope of the appended claims and their equivalents, the application can be practiced otherwise than as specifically described.

Claims

1. An optimization method for an industrial steam-green methanol collaborative production system, characterized in that: The method comprises: An interval parameter optimization model is constructed based on the cost objective function of the industrial steam-green methanol co-production system; the cost objective function includes equipment cost, operating cost, and carbon emission cost. The interval parameter optimization model is used to convert steam load fluctuations into interval variables to address the uncertainty of steam demand; Based on the uncertainty factors of the industrial steam-green methanol co-production system, a stochastic optimization model is constructed; the stochastic optimization model is used to generate a renewable energy output scenario tree based on the historical probability distribution of wind speed and solar radiation to deal with the random fluctuations of wind and solar power supply; Based on the preset interactive upper and lower bound solving strategy, the interval parameter optimization model and the random optimization model are solved to determine the target system parameters of the industrial steam-green methanol collaborative production system, and the industrial steam-green methanol collaborative production system is optimized based on the target system parameters.

2. The method according to claim 1, wherein Before constructing the interval parameter optimization model based on the cost objective function of the industrial steam-green methanol collaborative production system, the method further includes: Determining equipment cost parameters of the industrial steam-green methanol co-production system based on a preset discount rate and equipment life; Determining operating cost parameters of the industrial steam-green methanol collaborative production system based on a preset unit price coefficient; Determining the carbon emission cost parameters of the industrial steam-green methanol co-production system based on the carbon tax unit price and the total carbon emissions; The cost objective function is determined based on the equipment cost parameter, the operation cost parameter, and the carbon emission cost parameter.

3. The method according to claim 1, wherein The stochastic optimization model is constructed based on the uncertainty factors of the industrial steam-green methanol collaborative production system, including: The stochastic optimization model is constructed based on the joint probability distribution of historical wind speed time series and historical solar radiation intensity data.

4. The method according to claim 1, wherein The preset interactive upper and lower bound solving strategy is used to solve the interval parameter optimization model and the random optimization model, including: Based on the lower limit value of the steam demand interval, a lower bound sub-model is constructed to determine the first equipment configuration parameter and the first operating parameter; Based on the first equipment configuration parameters, an upper limit value of the steam demand range is obtained, and an upper bound sub-model is constructed to solve the second operating parameter; Based on the first operating parameters and the second operating parameters, target operating parameters of the industrial steam-green methanol collaborative production system are determined.

5. The method according to claim 1, wherein The target system parameters include target equipment parameters and target operating parameters. The target equipment parameters include the number of wind turbines, the area of ​​solar collectors and the rated power of electrolyzers. The target operating parameters include the feasible solution domain of steam flow, power distribution and methanol production.

6. The method according to claim 1, wherein The industrial steam-green methanol collaborative production system includes: Steam Energy Module, including industrial boilers, steam turbines, and pressure reducing valves for steam generation and distribution; Renewable energy modules, including wind turbines and solar thermal collectors, are used to convert wind energy into electricity and solar energy into low-pressure steam; The methanol production module includes an electrolyzer and a methanol synthesis reactor. The electrolyzer uses electricity to perform an electrolytic hydrogen production reaction to generate hydrogen; the methanol synthesis reactor uses carbon dioxide in industrial flue gas to react with the hydrogen to generate methanol.

7. The method according to claim 6, wherein The industrial boiler is used to convert the chemical energy of the fuel into thermal energy, generate high-temperature and high-pressure steam and output it to the steam turbine; The steam turbine is used to expand steam based on the steam from the industrial boiler, output mechanical energy, and distribute the steam to the pressure reducing valve; The pressure reducing valve is used to reduce the steam pressure based on preset inlet and outlet pressure thresholds, and output the reduced-pressure steam that meets the preset pressure conditions to the steam pipe network.

8. The method according to claim 6, wherein The wind turbine is used to generate wind power and output the generated electrical energy to the electrolyzer; The solar thermal collector is used to convert solar energy into thermal energy and output the generated steam to the steam network; The electrolyzer is used to perform a water electrolysis reaction based on the electrical energy of the wind turbine and output hydrogen to the methanol synthesis reactor; The methanol synthesis reactor is used to generate the methanol by carrying out a catalytic synthesis reaction based on the hydrogen in the electrolyzer and the carbon dioxide in the industrial flue gas.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

10. A computer storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

11. A computer program product comprising computer program instructions, characterized in that When the computer program instructions are executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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