A Global Heat Flux Topology Modeling and Optimization Method for Cold Energy Recovery and Regasification Processes in Large-Scale Floating Storage and Regasification Units

Abstract

This invention provides a global thermal flux topology modeling and optimization method for the cold energy recovery and regasification process of a large-scale floating storage and regasification unit. The method includes: establishing a preliminary thermal flux topology model by comparing the thermal flux topology based on the energy conservation of each system component and the inlet and outlet conditions; establishing a final global thermal flux topology model by eliminating redundant thermal flux loops based on Kirchhoff's laws and the thermocurrent method; determining the heat flow balance, thermal-hydraulic balance, working fluid type, system operating conditions, and various constraints of each system coupling link; solving the thermal flux topology model and comprehensively analyzing system performance; determining the objective function, setting the decision interval, and performing multi-objective optimization based on intelligent algorithms to solve for the optimal system operating parameters; and finally, introducing an FSRU system example to analyze and verify the accuracy of the example calculation results using the above method. This invention enables more sensitive fault detection, makes performance optimization more controllable, and effectively represents the synergistic effects in this large-scale system.

Description

Technical Field

[0001] This invention relates to the field of energy flow optimization technology, and more particularly to a global heat flow topology modeling and optimization method for the cold energy recovery and regasification process of a large-scale floating storage and regasification device. Background Technology

[0002] In recent years, global natural gas demand has steadily increased due to the low nitrogen oxide and carbon dioxide emissions and significant cost-effectiveness of internal combustion engines using natural gas as fuel. LNG (liquefied natural gas) offers significant advantages in natural gas storage and transportation, and is considered the most promising and efficient method.

[0003] Regasification is the final process in the natural gas supply chain. Compared with traditional onshore LNG receiving terminals, FSRUs have significant advantages in terms of construction time, relocation, and investment costs.

[0004] Because LNG is typically stored at -162°C, a significant amount of cold energy is released as waste heat into the surrounding seawater during regasification. However, most current regasification systems do not utilize this cold energy. If this cold energy could be efficiently utilized, it would significantly reduce energy waste and greenhouse gas emissions. Therefore, integrating LNG cold energy recovery into the FSRU regasification process is of great significance.

[0005] The Organic Rankine Cycle (ORC) can effectively capture the cold energy generated during LNG regasification and convert it into valuable electrical or mechanical energy. This improves energy utilization efficiency, reduces the demand for external power, and, moreover, the ORC system does not produce greenhouse gas emissions during operation. Therefore, combining the FSRU regasification system with a power generation system, utilizing the ORC cycle to form a combined cold energy recovery system, is a better solution for further recovering cold energy.

[0006] In recent years, various methods for recovering LNG cold energy based on different ORC configurations in FSRUs have emerged, mainly focusing on open-loop regasification systems in series (three stages) and cascade (four stages), with increasing system scale and complexity. Similarly, the performance evaluation and optimization methods for these systems share commonalities, revolving around the integration of system simulation and parametric analysis. They primarily rely on the physical analysis of basic components, analyzing the mass, momentum, and energy conservation equations for each part of the system, as well as related process equations. These equations are then combined based on component locations or system topology, and a global mathematical model is established based on the solutions to these equation sets. This modeling method is known as the simultaneous equations method (SEM) and the sequential modular method (SMM). However, with the increasing complexity of thermodynamic systems, the simultaneous existence of numerous constraints makes traditional simulation methods difficult, thus requiring an efficient modeling and simulation method. Against this backdrop, the heat flow method has been introduced to analyze and optimize thermodynamic systems. It uses circuit principles to characterize the system, naturally presenting the various constraints of the global system, and allows for global optimization using various algorithms, improving modeling accuracy and providing a more detailed description of underlying physical conditions. Meanwhile, heat flow systems can effectively circumvent the inherent non-convexity problem of nonlinear optimization. Therefore, heat flow tools are increasingly widely used in modeling and simulating various small-scale thermal systems. However, the heat flow method still faces many problems and challenges in global modeling and optimization of large-scale thermal systems: First, the reliability of the heat flow method needs to be guaranteed by verifying equivalence and consistency with component analysis; second, more efficient algorithms are still needed to solve nonlinear equation systems; third, solving all constraint equation systems simultaneously in large-scale thermal systems is extremely difficult; fourth, a proprietary algorithm needs to be developed to improve computational efficiency and stability; fifth, the system needs to integrate multiple methods and tools, such as heat flow tools, numerical simulation tools, heat transfer analysis methods, global network thermal resistance analysis, genetic algorithms, and approximation of ideal solutions sorting methods.

[0007] Therefore, based on the above problems, this invention proposes a novel global heat flux topology modeling and optimization method for the cold energy recovery and regasification process of large-scale floating storage and regasification devices. Summary of the Invention

[0008] To address the aforementioned technical problems, this invention provides a global heat flux topology modeling and optimization method for the cold energy recovery and regasification process in large-scale floating storage and regasification units. This invention details the large-scale LNG cold energy recovery and regasification process in a floating storage and regasification unit. Verification through modeling and simulation results confirms the accuracy and reliability of the proposed model. Furthermore, the impact of key parameters on system performance is analyzed in depth, system sensitivity and potential optimization approaches are discussed, and the optimal system operating point is derived.

[0009] The technical means employed in this invention are as follows:

[0010] A global heat flux topology modeling and optimization method for cold energy recovery and regasification processes in a large-scale floating storage and regasification unit includes:

[0011] Step 1: Based on the energy conservation of each component of the system and the inlet and outlet conditions, compare the heat flow topology and establish the first model, which is the preliminary heat flow topology model;

[0012] Step 2: Combining the first model with Kirchhoff's laws and the thermocurrent method, eliminate redundant heat flow loops and establish the second model, which is the final global heat flow topology model.

[0013] Step 3: Establish the third model, which is used to determine the heat flow balance, thermal-hydraulic balance, working fluid type, system operating conditions, and various constraints of the coupling links of each system.

[0014] Step 4: Based on the second and third models, solve the heat flux topology model and comprehensively analyze the system performance; the system performance analysis includes energy analysis, Analysis and heat transfer analysis of pyroclastic deposits, etc.;

[0015] Step 5: Based on Step 4, determine the objective function, set the decision interval, and perform multi-objective optimization based on intelligent algorithms to solve for the optimal operating parameters of the system; the objective function includes thermodynamic functions such as efficiency, exergy, and dissipation.

[0016] Step 6: Introduce an FSRU system instance and analyze the methods from Steps 1 to 5 to verify the accuracy of the instance calculation results.

[0017] Furthermore, in step one, the modeling method for the preliminary heat flow topology model includes:

[0018] S1. Based on the comprehensive global heat flow topology model of a large-scale thermal system, the basic components of the thermal system, namely heat exchangers, evaporators, condensers, mixers, and recondensers, are equivalent to heat flow models that include thermal resistance. Among them, the heat flow model that includes thermal resistance has a unique configuration based on the fact that three fluids flow through the recondenser.

[0019] S2. Based on the comprehensive global heat flow topology model of a large-scale thermal system, the basic components of the drive system, namely the throttle valve, drive pump, compressor, turbine and separator, are equivalent to a heat flow model that includes thermodynamic potential.

[0020] S3. By combining the component models in S1 and S2 and describing the system's thermal topology, a preliminary global thermal topology model is established, namely the preliminary thermal topology model.

[0021] Furthermore, in step two, the method for establishing the final global heat flow topology model is as follows: when constructing the heat flow model, based on Kirchhoff's laws and according to the thermocurrent method, the closed loop consisting only of thermodynamic potential represents the temperature change in the closed loop, which is eliminated without changing the basic properties of the system, thus establishing the final global heat flow topology model.

[0022] Furthermore, in step three, the heat flow balance constraints include energy flow topology constraints, energy conservation constraints, energy flow transfer and conversion constraints, and component constraints; among them, the energy conservation equation describing the heat-work conversion process of the entire system is obtained using the KCL equations; the heat transfer and conversion within the system is reflected by utilizing the heat flow method through the KVL equation set; the component constraints include heat exchanger constraints, turbine constraints, compressor constraints, and pump set constraints.

[0023] The heat exchanger constraints are: based on the thermal resistance and thermodynamic potential of the heat flow method, the heat exchanger under the design conditions is simulated by combining Aspen HYSYSYS V11 software and MATLAB software to obtain the heat exchange performance parameters.

[0024] The turbine constraints are constraint equations describing the heat-work conversion process in the turbine. Since there are irreversible losses caused by irreversible factors in each process of the actual turbine cycle, the influence of irreversible factors on cycle performance is considered, which is the turbine element constraint.

[0025] Furthermore, in step three, the thermal-hydraulic balance constraints include constraints on the turbine and the pump; wherein, the thermal-hydraulic balance constraints of the pump are obtained by the following method: simulating the design operating conditions of the pump using Aspen HYSYSYS V11 software and MATLAB software, obtaining the corresponding thermal-hydraulic performance parameters for a given evaporation temperature range, and finally fitting the parameters with a quadratic polynomial to obtain the thermal-hydraulic balance constraints of the pump.

[0026] The working fluid type constraint refers to the analysis of the working fluid's thermophysical properties. These properties are derived from fundamental thermodynamic state parameters based on inherent functional relationships. Specifically, when the pressure during the phase change process is known, the phase change temperature and latent heat of phase change are used as fundamental thermophysical parameters and directly derived from these inherent functional relationships. Three cases are considered for the specific heat capacity of the working fluid: for organic working fluids in ORC systems, a constant specific heat capacity assumption is adopted; for LNG, since its specific heat capacity is more sensitive to temperature changes, a quadratic polynomial fitting is used to obtain specific heat capacity values ​​for different decision variable ranges; for other working fluids, the average specific heat capacity is used to calculate the specific heat capacity under different operating conditions.

[0027] Furthermore, in step four, the energy analysis includes solving for net power generation, system consumption ratio, system thermal efficiency, and specific net output power. First, net power generation is calculated to ensure that system power generation exceeds power consumption to meet emission reduction targets. Then, the system consumption ratio is calculated based on net power generation to obtain the proportional relationship between system power generation and demand. Next, system thermal efficiency is calculated based on net power generation to determine the energy utilization efficiency during the system's thermal energy conversion process. Finally, specific net output power is calculated based on net power generation to reflect the utilization of LNG cold energy under different operating conditions.

[0028] The The analysis includes solving the LNG cold Efficiency is used to evaluate the degree of cold energy recovery in a system and the heat exchange efficiency of the condenser.

[0029] In the analysis of heat transfer in the fire accumulation zone, the associated work, associated heat, associated heat dissipation, and net fire accumulation transfer in the cycle are redefined, thereby leading to the system performance evaluation index, namely the ratio of fire accumulation transfer benefit to fire accumulation transfer cost.

[0030] Furthermore, in step five, based on step four, the most efficient system configuration is obtained by analyzing the changes in system performance parameters under the influence of a single decision variable; wherein, the single decision variable includes LNG pressure, seawater temperature, and evaporation temperature.

[0031] Furthermore, in step five, based on the most efficient system configuration described above, the system performance parameters under the influence of dual decision variables are analyzed to obtain the changes in the performance parameters of the selected system configuration under the synergistic influence of the dual decision variables; wherein, the dual decision variables include LNG pressure and seawater temperature, as well as LNG pressure and evaporation temperature.

[0032] Furthermore, in step five, a multi-objective optimization method is used to select two parameters describing the overall system performance: thermal efficiency and pyroelectric transfer efficiency. Thermal efficiency represents the system's ability to convert thermal energy into mechanical work, while pyroelectric transfer efficiency characterizes the system's heat transfer capacity. Within the range of decision variables, the two performance parameters, thermal efficiency and pyroelectric transfer efficiency, conflict with each other. To balance thermal efficiency and pyroelectric transfer efficiency, a genetic algorithm is used to optimize the two objectives of the system and obtain the Pareto front. Then, the TOPSIS multi-criteria decision method is applied to perform multi-objective optimization for different evaporator temperatures and optimize the system performance parameters for different seawater temperatures within the decision interval, thereby obtaining the optimal operating point.

[0033] Furthermore, in step six, the modeling and simulation results are verified using the FSRU case and the Aspen HYSYS V11 software to simulate component parameters:

[0034] First, six different organic working fluid configurations were selected to evaluate the overall performance of large-scale LNG cold energy recovery and regasification in FSRUs. The most suitable organic working fluid configuration was determined by analyzing the system performance parameters under the influence of a single decision variable. A bubble diagram of the overall thermal resistance distribution of the FSRU was plotted to represent the change in total thermal resistance of the optimal configuration under different operating conditions.

[0035] Secondly, an analysis of the system performance parameters under the influence of two decision variables is conducted for this configuration;

[0036] Finally, a multi-objective optimization method was used to select two parameters describing the overall performance of the system: thermal efficiency and pyrolysis transfer efficiency. A genetic algorithm was used to balance the two parameters, and then the TOPSIS multi-criteria decision method was used to obtain the optimal operating point.

[0037] After comparison, all errors were within 5.6%, thus confirming the accuracy and reliability of the system model.

[0038] Compared with the prior art, the present invention has the following advantages:

[0039] 1. The large-scale LNG cold energy recovery and regasification process in the floating storage and regasification device of this invention adopts a three-stage ORC system, which realizes carbon emission reduction.

[0040] 2. This invention integrates multiple methods and tools, including heat flow tools, numerical simulation tools, pyrolysis transfer analysis methods, global network thermal resistance analysis, GA, and TOPSIS. This not only fully reflects the overall physical condition of large-scale thermodynamic systems, ensuring the accuracy of modeling and improving the stability of calculations, but also satisfies the comprehensiveness of parameter analysis.

[0041] 3. This invention addresses energy, The thermal system is comprehensively evaluated from multiple aspects, including heat transfer, key parameter analysis, and multi-objective optimization.

[0042] 4. The modeling and simulation of the regasification process in a large-scale LNG cold energy recovery and regasification unit of this invention involves the construction of a global power flow model, the development of a mathematical model, the analysis of specific parameters, and multi-objective optimization. This comprehensive approach enables more sensitive fault detection and makes performance optimization more controllable, thereby effectively representing the synergistic effects in this large-scale system.

[0043] 5. This invention employs visualization techniques to enhance the understanding of large-scale thermodynamic systems. First, a global power flow model is presented when the system performance parameters are unknown. Then, after determining the performance parameters, the energy flow characteristics of the system are analyzed using a thermal resistance bubble diagram.

[0044] Based on the above reasons, this invention can be widely applied in fields such as energy flow optimization. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This is a flowchart of the method of the present invention.

[0047] Figure 2 This is the equivalent physical model of the recondenser.

[0048] Figure 3 This is a heat flow topology model containing thermal resistance elements.

[0049] Figure 4 This is a thermal flow topology model for components driven by thermodynamics.

[0050] Figure 5 Flowchart of a global heat flux topology modeling and optimization method for cold energy recovery and regasification processes in large-scale floating storage and regasification units.

[0051] Figure 6 This is a flowchart of the cold energy recovery and regasification process of a large and complex FSRU system.

[0052] In the diagram: 1. Storage tank; 2. Forced evaporator; 3. Mixer; 4. Separator; 5. Low-load compressor; 6. Gas cooler / heater; 7. First pressure reducing valve; 8. Second pressure reducing valve; 9. Re-condenser; 10. First drive pump; 11. First condenser; 12. First generator; 13. First turbine; 14. Second condenser; 15. Second drive pump; 16. First evaporator; 17. Second turbine; 18. Second generator; 19. Third condenser; 20. Third drive pump; 21. Second evaporator; 22. Third turbine; 23. Third generator; 24. Fourth condenser; 25. Fourth drive pump; 26. Third evaporator; 27. Heater; 28. Fourth turbine; 29. ​​Fourth generator; 30. Seawater pump. Detailed Implementation

[0053] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0054] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the present invention or its application or use. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0056] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the invention. It should also be understood that, for ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale. Techniques, methods, and devices known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and devices should be considered part of the specification. In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters in the following figures denote similar items; therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0057] In the description of this invention, it should be understood that the orientation or positional relationship indicated by directional terms such as "front, back, up, down, left, right", "horizontal, vertical, horizontal" and "top, bottom" is generally based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing this invention and simplifying the description. Unless otherwise stated, these directional terms do not indicate or imply that the device or element referred to must have a specific orientation or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on the scope of protection of this invention. The directional terms "inner" and "outer" refer to the inner and outer contours relative to the outline of each component itself.

[0058] For ease of description, spatial relative terms such as "above," "over," "on the upper surface of," "above," etc., are used herein to describe the spatial positional relationship of a device or feature as shown in the figures to other devices or features. It should be understood that spatial relative terms are intended to encompass different orientations in use or operation besides the orientation of the device as described in the figures. For example, if the device in the figures is inverted, a device described as "above" or "above" other devices or structures would subsequently be positioned as "below" or "under" other devices or structures. Thus, the exemplary term "above" can include both "above" and "below." The device may also be positioned in other different ways (rotated 90 degrees or in other orientations), and the spatial relative descriptions used herein will be interpreted accordingly.

[0059] Furthermore, it should be noted that the use of terms such as "first" and "second" to define components is merely for the purpose of distinguishing the corresponding components. Unless otherwise stated, the above terms have no special meaning and therefore should not be construed as limiting the scope of protection of this invention.

[0060] The technical problem this invention aims to solve is to provide a global heat flux topology modeling and optimization method for the cold energy recovery and regasification process in a large-scale floating storage and regasification unit. It details the large-scale LNG cold energy recovery and regasification process in a floating storage and regasification unit, and verifies the accuracy and reliability of the proposed model through modeling and simulation results. Furthermore, it analyzes the impact of key parameters on system performance, discusses system sensitivity and potential optimization approaches, and derives the optimal system operating point.

[0061] like Figure 1 As shown, the present invention discloses a global thermal flux topology modeling and optimization method for cold energy recovery and regasification processes in a large-scale floating storage and regasification device, including a summary diagram of global thermal flux topology modeling and optimization for complex systems, specifically comprising the following steps:

[0062] Step 1: Based on the energy conservation of each component in the system and the inlet and outlet conditions, compare the heat flow topology and establish a preliminary heat flow topology model;

[0063] Step 2: Combining the preliminary heat flow topology model, based on Kirchhoff's laws and the thermocurrent method, eliminate redundant heat flow loops and establish the final global heat flow topology model.

[0064] Step 3: Determine the heat flow balance, thermal-hydraulic balance, working fluid type, system operating conditions, and various constraints of the coupling links of each system;

[0065] Step 4: Based on the final global heat flow topology model and heat flow balance, thermal-hydraulic balance, working fluid type, system operating conditions, and various constraints of each system coupling link, solve the heat flow topology model and comprehensively analyze the system performance (energy analysis, ...). Analysis and heat transfer analysis of pyroclastic deposits, etc.;

[0066] Step 5: Based on Step 4, determine the objective function (such as thermodynamic functions like efficiency, exergy, and dissipation), set the decision interval, and perform multi-objective optimization based on intelligent algorithms to solve for the optimal operating parameters of the system.

[0067] Step 6: Introduce an FSRU system instance and analyze and verify the accuracy of the instance calculation results using the methods described above.

[0068] As a preferred embodiment, the modeling method for the preliminary heat flow topology model in step one includes: based on a comprehensive global heat flow topology model of a large-scale thermal system, the basic components of the thermal system—heat exchanger, evaporator, condenser, mixer, and recondenser—are equivalent to heat flow models that include thermal resistance. Notably, because three fluids flow through the recondenser, it has a unique configuration.

[0069] The modeling method for the preliminary heat flux topology model described in step one includes: based on a comprehensive global heat flux topology model of a large-scale thermodynamic system, the basic components of the driving system—throttle valve, drive pump, compressor, turbine, and separator—are equivalent to heat flux models containing thermodynamic potentials. Then, through the combination of the component models and the description of the system's heat flux topology, a preliminary global heat flux topology model is established.

[0070] As a preferred implementation, in step two, the establishment of the final global heat flux topology model is based on the principle that, in circuit theory, a closed loop consisting only of electromotive force is usually assumed to have zero internal current, thus eliminating this loop. Similarly, according to the principle of analogy, in constructing the heat flux model, based on the thermocurrent method, a closed loop consisting only of thermodynamic potential represents the temperature change within the closed loop and can be eliminated without changing the fundamental properties of the system. This simplifies the model while ensuring its accuracy and reliability, making it easier to understand and establishing the final global heat flux topology model.

[0071] In a preferred embodiment, the heat flow constraints in step three include energy flow topology constraints, energy conservation constraints, energy flow transfer and conversion constraints, and component constraints. The energy conservation equation describing the entire system's heat-work conversion process is obtained using the KCL equations. Similarly, the KVL equations, through the use of the heat flow method, reflect the heat transfer and conversion within the system. In the component constraints, the heat exchanger's constraint equations are based on the thermal resistance and thermodynamic potential of the heat flow method. By combining AspenHYSYSYS V11 and MATLAB software, the heat exchanger under design conditions is simulated to obtain heat transfer performance parameters. Since irreversible losses occur in each process of the actual turbine cycle, the impact of irreversible factors on cycle performance needs to be considered, which constitutes turbine component constraints. Furthermore, the component constraints also include compressor constraints and pump set constraints.

[0072] The aforementioned thermal-hydraulic balance constraints primarily refer to the constraints on the steam turbine. Furthermore, for the thermal-hydraulic balance constraints of the pump, the design operating conditions of the pump were simulated using Aspen HYSYSYS V11 and MATLAB software. Subsequently, for a given evaporation temperature range, the corresponding thermal-hydraulic performance parameters were obtained. Finally, a quadratic polynomial was used to fit the parameters to obtain the thermal-hydraulic balance constraints of the pump.

[0073] The constraint of the working fluid type refers to the analysis of its thermodynamic properties. The thermodynamic properties of the working fluid can be derived from basic thermodynamic state parameters based on inherent functional relationships. When the pressure during the phase change process is known, the phase change temperature and latent heat of phase change, as basic thermodynamic property parameters, can be directly obtained from the aforementioned inherent functional relationships. For the specific heat capacity of the working fluid, three cases need to be considered: for organic working fluids in ORC systems, a constant specific heat capacity assumption is usually adopted; for LNG, because its specific heat capacity is more sensitive to temperature changes, quadratic polynomial fitting is often used to obtain specific heat capacity values ​​for different decision variable ranges; for other working fluids, the average specific heat capacity is often used to calculate the specific heat capacity under different operating conditions.

[0074] In a preferred embodiment, the system performance analysis in step four mainly focuses on three aspects, including energy analysis, Analysis and heat transfer analysis of pyroclastic deposits.

[0075] The energy analysis first requires solving the net power generation to ensure that the system's power generation exceeds its power consumption to meet emission reduction targets. Second, it requires solving the system consumption ratio to obtain the proportional relationship between the system's power generation and power demand. Third, it requires solving the system thermal efficiency to obtain the energy utilization efficiency in the system's thermal energy conversion process. In addition, it requires solving the net output power ratio to reflect the utilization of LNG cold energy under different operating conditions.

[0076] The Analyze demand for LNG cooling Efficiency is used to evaluate the degree of cold energy recovery in a system and the heat exchange efficiency of the condenser.

[0077] The heat transfer analysis of pyroclastic deposits employs a novel analytical method. Compared to traditional pyroclastic loss analysis methods, this method extends the analysis from a hypothetical heating process to an actual heating process, thereby resolving the controversy surrounding the definition of (pyroclastic deposits) in thermodynamics. This analysis redefines associated work, associated heat, associated heat dissipation, and net pyroclastic transfer in the cycle. This leads to the introduction of a system performance evaluation index: the ratio of pyroclastic transfer benefits to pyroclastic transfer costs.

[0078] As a preferred implementation, in step five, solving for the optimal operating parameters of the system requires, based on step four, analyzing the changes in system performance parameters under the influence of a single decision variable, where the single decision variable includes LNG pressure, seawater temperature, and evaporation temperature, in order to obtain the most efficient system configuration.

[0079] In a preferred embodiment, the solution to the optimal operating parameters of the system in step five requires further analysis of the system performance parameters under the influence of two decision variables based on the above system configuration. The two decision variables include: LNG pressure and seawater temperature, and LNG pressure and evaporation temperature, so as to obtain the changes in the performance parameters of the selected system configuration under the synergistic influence of the two decision variables.

[0080] In a preferred embodiment, step five, which involves solving for the optimal system operating parameters, employs a multi-objective optimization method to select two parameters that describe the overall system performance: thermal efficiency and heat transfer efficiency. The former represents the system's ability to convert thermal energy into mechanical work, while the latter characterizes the system's heat transfer capacity. However, these two performance parameters conflict with each other, requiring a trade-off. Therefore, to achieve this balance, a genetic algorithm is used to optimize the two system objectives and obtain the Pareto front. Subsequently, the TOPSIS multi-criteria decision-making method is applied to perform multi-objective optimization for different evaporator temperatures and system performance parameters for different seawater temperatures within the decision interval, thereby selecting the optimal operating point.

[0081] In a preferred implementation, step six utilizes an FSRU case study, combined with Aspen HYSYS V11 software to simulate component parameters and verify the modeling and simulation results. First, six different organic working fluid configurations are selected to evaluate the overall performance of large-scale LNG cold energy recovery and regasification in the FSRU. The optimal organic working fluid configuration is determined by analyzing the system performance parameters under the influence of a single decision variable. Furthermore, a bubble diagram of the overall thermal resistance distribution of the FSRU is plotted to represent the total thermal resistance variation of the optimal configuration under different operating conditions. Second, the system performance parameters under the influence of two decision variables are analyzed for this configuration. Finally, a multi-objective optimization method is used to select two parameters that can describe the overall system performance: thermal efficiency and transmission efficiency. The GA algorithm is used to balance these two parameters, and then the TOPSIS multi-criteria decision method is used to obtain the optimal operating point. Comparison shows that all errors are within 5.6%, thus confirming the accuracy and reliability of the system model.

[0082] Example 1

[0083] like Figure 5 As shown, this invention provides a global heat flux topology modeling and optimization method for the cold energy recovery and regasification process of a large-scale floating storage and regasification device:

[0084] Step 1: Based on the energy conservation of each component in the system and the inlet and outlet conditions, compare the heat flow topology and establish a preliminary heat flow topology model;

[0085] Among them, such as Figure 3 As shown, based on the comprehensive global heat flow topology model of a large-scale thermal system, the basic components of the thermal system—heat exchangers, evaporators, condensers, mixers, and recondensers—are equivalent to heat flow models that include thermal resistance. For heat exchangers with phase change processes, the thermal resistance model is as follows:

[0086]

[0087]

[0088] In the formula, (KA) c Heat transfer coefficient (W / m) 2 K), G c The heat capacity rate (W / K) of the working fluid flowing through the device, with the subscript pch indicating the phase transition stage, R pch,CON R is the thermal resistance (K / W) during the phase change stage of the condenser. pch,CON The thermal resistance (K / W) during the phase change stage of the evaporator.

[0089] Among them, such as Figure 2 As shown, the recondenser has a unique configuration because three fluids flow through it.

[0090] Among them, such as Figure 4 As shown, the basic components of the drive system—throttle valve, drive pump, compressor, turbine, and separator—are equivalent to heat flow models incorporating thermodynamic potential. Furthermore, by combining the component models and describing the system's heat flow topology, a preliminary global heat flow topology model is established.

[0091] Step 2: Describe the FSRU system: where, for example Figure 6 As shown, the FSRU regasification system in this example is based on ORC-OC technology. The system consists of three subsystems: a BOG (evaporated gas) subsystem, a three-stage ORC subsystem, and an open-loop ORC(OC) subsystem.

[0092] In the BOG subsystem, excess flash vapor generated by LNG undergoes recondensation. This recondensation process occurs in the recondenser (RECD), where BOG and LNG come into contact indirectly via a heat exchanger. This indirect heat transfer allows the liquefied natural gas to transition back to a liquid state. The newly liquefied LNG is then pressurized for external transport. The BOG subsystem includes a forced evaporator 2, whose LNG inlet is connected to the outlet of storage tank 1. The BOG outlet of forced evaporator 2 is connected to the BOG inlet of mixer 3, which is also connected to the outlet of storage tank 1. The BOG outlet of mixer 3 is connected to the inlet of separator 4, which is connected to the inlet of low-load compressor 5. The outlet of low-load compressor 5 is connected to the BOG inlet of gas cooler / heater 6, which is connected to the inlet of first pressure reducing valve 7. The outlet of gas cooler / heater 6 is connected to the inlet of a dual-fuel engine or a gas combustion unit.

[0093] The OC subsystem, serving as an auxiliary power generation system, further increases the power supply of the FSRU system. The OC subsystem includes a first pressure-reducing valve 7, whose outlet is connected to the BOG inlet of the recondenser 9; a second pressure-reducing valve 8, whose inlet is connected to the outlet of storage tank 1; whose outlet is connected to the LNG inlet of the recondenser 9; the recondenser 9 outlet is connected to the inlet of the first drive pump 10; the first drive pump 10 outlet is connected to the LNG inlet of the first condenser 11; the LNG outlet of the first condenser 11 is connected to the LNG inlet of the second condenser 14; the first turbine 13 inlet is connected to the outlet of heater 27; the kinetic energy outlet of the first turbine 13 is connected to the inlet of the first generator 12; the NG outlet of the first turbine 13 is connected to the NG inlet of the first condenser 11; and the LNG outlet of the first condenser 11 is connected to the LNG inlet of the recondenser 9.

[0094] The three-stage ORC subsystem effectively utilizes the cold energy contained in LNG to meet the power requirements of the FSRU. The three-stage ORC subsystem includes a second condenser 14, whose NG outlet is connected to the NG inlet of a third condenser 19; its organic working fluid (CF) outlet is connected to the inlet of a second drive pump 15; the outlet of the second drive pump 15 is connected to the CF inlet of a first evaporator 16; the CF outlet of the first evaporator 16 is connected to the inlet of a second turbine 17; the kinetic energy outlet of the second turbine 17 is connected to the inlet of a second generator 18; the CF outlet of the second turbine 17 is connected to the CF inlet of the second condenser 14; the NG outlet of the third condenser 19 is connected to the NG inlet of a fourth condenser 24; the CF outlet of the third condenser 19 is connected to the inlet of a third drive pump 20; the outlet of the third drive pump 20 is connected to the CF inlet of a second evaporator 21; the CF outlet of the second evaporator 21 is connected to the inlet of a third turbine 22; and the kinetic energy outlet of the third turbine 22 is connected to the third generator 28. The CF outlet of the third turbine 22 is connected to the CF inlet of the third condenser 19. The NG outlet of the fourth condenser 24 is connected to the NG inlet of the heater 27. The NG outlet of the heater 27 is connected to the external natural gas pipeline and the inlet of the first turbine 13. The CF outlet of the fourth condenser 24 is connected to the inlet of the fourth drive pump 25. The outlet of the fourth drive pump 25 is connected to the CF inlet of the third evaporator 26. The CF outlet of the third evaporator 26 is connected to the inlet of the fourth turbine 28. The kinetic energy outlet of the fourth turbine 28 is connected to the inlet of the fourth generator 29. The CF outlet of the fourth turbine 28 is connected to the CF inlet of the fourth condenser 24. The outlet of the seawater pump 30 is connected to the seawater inlets of the first evaporator 16, the second evaporator 21, the third evaporator 26, and the heater 27. The seawater outlets of the first evaporator 16, the second evaporator 21, the third evaporator 26, and the heater 27 are connected to the drainage pipeline.

[0095] Step 3: Based on Kirchhoff's laws and the thermocurrent method, eliminate redundant heat flow loops and establish the final global heat flow topology model. In circuit theory, a closed loop consisting only of electromotive force is usually assumed to have zero internal current, thus eliminating this loop. Similarly, based on the principle of analogy, in constructing the heat flow model, according to the thermocurrent method, a closed loop consisting only of thermodynamic potential represents the temperature change within the closed loop and can be eliminated without changing the fundamental properties of the system. This simplifies the model while ensuring its accuracy and reliability, making it easier to understand and establishing the final global heat flow topology model.

[0096] Step 4: Determine the heat flow balance, thermal-hydraulic balance, working fluid type, system operating conditions, and various constraints of the coupling links of each system.

[0097] The heat flow balance constraints include energy flow topology constraints, energy conservation constraints, energy flow transfer and conversion constraints, and component constraints; among them, the energy conservation equation describing the heat-work conversion process of the entire system is obtained using the KCL equation:

[0098]

[0099] In the formula, W GE-1 W GE-2 W GE-3 and W GE-4 The first, second, third, and fourth generators respectively perform work (J); Q pch,MX and Q sh,MX These represent the heat transfer rates (W / mK) during the phase change and superheating stages of the mixer, respectively; Q pc,BOG Q sh,BOG and Q pch,BOG These represent the heat transfer rates during the precooling, superheating, and phase change stages of the volatile gas, respectively; Q LNG,H The heat transfer rate of high-temperature LNG (W / mK); Q pc,CON-1 Q pch,CON-1 Q pc,CON-2 Q pch,CON-2 Q pc,CON-3 Q pch,CON-3 Q pc,CON-4 and Q pch,CON-4 The heat transfer rates (W / mK) for the precooling and phase change stages of the first, second, third, and fourth condensers, respectively; W in,LNG The input power (J) of LNG; W in,LDC Input power (J) to the low-load compressor; W in,FP The input power (J) of the feed pump; W P-1 W P-2 W P-3 and W P-4 The first, second, third, and fourth drive pumps respectively perform work (J); Q sh,VAP-1 Q pch,VAP-1 Q sh,VAP-2 Q pch,VAP-2 Q ph,VAP-2 Q sh,VAP-3 Q pch,VAP-3 and Q ph,VAP-3 The heat transfer rates (W / mK) for the superheating, phase change, and preheating stages of the first, second, and third evaporators, respectively; Q TH The heater heat transfer rate is denoted as W / mK.

[0100] The KVL equations demonstrate heat transfer and conversion within the system by utilizing the heat flow method:

[0101]

[0102] In the formula, TLNG,1 ε represents the temperature (K) of the LNG in the storage tank. LNG,1 ε represents the thermodynamic potential (K) of the LNG in the storage tank; V-1 The thermodynamic potential (K) of the first pressure-reducing valve; Q sc,BOG R is the heat transfer rate during the subcooling stage of the volatile gas (W / mK); sc,BOG Thermal resistance (K / W) during the subcooling stage of the volatile gas; ε sc,8,9 The thermodynamic potential (K) between the second pressure reducing valve and the recondenser under subcooled conditions; ε 9,11 The thermodynamic potential (K) between the recondenser and the first condenser; ε sc,10,11 The thermodynamic potential (K) between the first compressor and the first condenser under subcooled conditions; ε pch,10,11 ε is the thermodynamic potential (K) under the phase change state between the first compressor and the first condenser; pch,11,14 ε is the thermodynamic potential (K) under the phase transition state between the first and second condensers; pc,14,19 The thermodynamic potential (K) under pre-cooling conditions between the second and third condensers; ε pch,14,19 The thermodynamic potential (K) under the phase transition state between the second and third condensers; ε pc,19,24 The thermodynamic potential (K) under pre-cooling conditions between the third and fourth condensers; ε pch,19,24 The thermodynamic potential (K) under the phase transition state between the third and fourth condensers; ε pc,24,27 Q represents the thermodynamic potential (K) under pre-cooling conditions between the third and fourth condensers; TH R is the heat transfer rate of the heater (W / mK); TH ε is the thermal resistance of the heater (K / W); SW,in The thermal kinetic potential (K) at the seawater inlet; T SW,in The input seawater temperature (K).

[0103] The component constraints include heat exchanger constraints, which primarily consist of the thermal resistance and thermodynamic potential constraint equations based on the thermocurrent method mentioned above. Furthermore, the heat transfer performance parameters of a single-phase heat exchanger are represented by thermal conductivity (KA), while those of a phase-change heat exchanger are represented by the overall heat transfer coefficient (K). That is, given the range of variation of a single decision variable, the heat transfer performance parameters of the heat exchanger at different state points are simulated. Finally, a quadratic polynomial is used to fit the data to obtain the overall heat transfer coefficient (K) or thermal conductivity (KA) under this non-design condition. The fitting equation is as follows:

[0104]

[0105]

[0106]

[0107] In the formula, p is the pressure (Pa); K(p) represents the overall heat transfer coefficient (W / m³) when pressure is the only decision variable. 2 K); K0 is the heat transfer coefficient in the initial state (W / m). 2 K); T0 is the initial temperature; p0 is the initial pressure (Pa); SW represents seawater; T SW Seawater temperature; K(T) SW The overall heat transfer coefficient (W / m³) is represented when seawater temperature is the only decision variable. 2 K); e represents the evaporator; T e K(T) represents the evaporator temperature. e The overall heat transfer coefficient (W / m³) is represented when evaporation temperature is the only decision variable. 2 K); a, b, c are characteristic parameters respectively.

[0108] For heat exchangers in single-phase and phase-change states, the heat transfer area constraint equation is as follows:

[0109] A VAP-i =A ph,VAP-i +A pch,VAP-i +A sh,VAP-i i = 1, 2, 3;

[0110] A CON-i =A ph,CON-i +A pch,CON-i +A sh,CON-i , i = 1, 2, 3, 4;

[0111] In the formula, the subscript ph represents the preheating stage, sh represents the superheating stage, and pc represents the precooling stage; A VAP-i The heat transfer area (m²) of the i-th evaporator 2 A ph,VAP-i The heat transfer area (m²) of the i-th evaporator under preheating conditions 2 A pch,VAP-i Let be the heat transfer area (m²) of the i-th evaporator under phase change state. 2 A sh,VAP-i Let be the heat transfer area (m²) of the i-th evaporator under superheated conditions. 2 A CON-i The heat transfer area (m²) of the i-th condenser 2 A ph,CON-i The heat transfer area (m²) of the i-th condenser under preheated conditions 2 A pch,CON-i Let be the heat transfer area (m²) of the i-th condenser under phase change state. 2 A sh,CON-i The heat transfer area (m²) of the i-th condenser under superheated conditions 2 ).

[0112] The energy-saving equation for the phase change process of the heat exchanger is shown below:

[0113] Q ph,VAP-i =m SW,VAP-i γ pch,VAP-i i = 1, 2, 3;

[0114] Q VAP-i =G SW,VAP-i (T SW,in -T SW,out ), i = 1, 2, 3;

[0115] Q pch,CON-i =m NG,CON-i γ pch,CON-i , i = 1, 2, 3, 4;

[0116] In the formula, Q ph,VAP-i Q represents the heat transfer rate (W / mK) of the i-th evaporator under preheated conditions; VAP-i Q is the heat transfer rate (W / mK) of the i-th evaporator; pch,VAP-i Let m be the heat transfer rate (W / mK) in the phase change state of the i-th evaporator; SW,VAP-i The mass (kg) of seawater flowing through the i-th evaporator; γ pch,VAP-i G represents the latent heat of vaporization of the i-th evaporator under phase change conditions. SW,VAP-i T is the heat capacity factor (W / K) of seawater flowing through the i-th evaporator; SW,in and T SW,out These represent the temperatures (K) of the seawater entering and exiting the evaporator, respectively; m NG,CON-i The flow rate (kg / s) of a unit mass of gaseous natural gas flowing through the i-th condenser d.

[0117] Among these, component constraints also include turbine constraints. The constraint equations describing the heat-work conversion process in the turbine are as follows:

[0118]

[0119]

[0120]

[0121]

[0122] In the formula, η T-1 η T-2 η T-3 and η T-4 The efficiencies of the first, second, third, and fourth steam turbines, respectively; h T-1 h T-2 h T-3 and h T-4Specific enthalpy (J / kg) for the first, second, third, and fourth steam turbines, respectively; p NG,T-1,in and p NG,T-1,out These represent the natural gas pressures (Pa) flowing into and out of the first turbine, respectively; T NG,T-1,in and T NG,T-1,out These represent the temperatures (K) of the natural gas flowing into and out of the first turbine, respectively; T NG,T-1,out,s p represents the temperature of the natural gas flowing out of the first steam turbine under isentropic conditions. cf,T-2,in p cf,T-3,in and p cf,T-4,in These represent the pressures (Pa) of the circulating fluid flowing into the second, third, and fourth turbines, respectively; p cf,T-2,out p cf,T-3,out and p cf,T-4,out These represent the pressures (Pa) of the circulating fluid flowing out of the second, third, and fourth turbines, respectively; T cf,T-2,in T cf,T-3,in and T cf,T-4,in These are the temperatures (K) of the circulating fluid flowing into the second, third, and fourth turbines, respectively; T cf,T-2,out T cf,T-3,out and T cf,T-4,out These are the temperatures (K) of the circulating fluid flowing out of the second, third, and fourth turbines, respectively; T cf,T-2,out,s T cf,T-3,out,s and T cf,T-4,out,s These are the temperatures (K) at which the circulating fluid from the second, third, and fourth turbines flows out under isentropic conditions.

[0123] Among these, component constraints also include compressor constraints. Due to the existence of compressor adiabatic efficiency, the compressor's heat-work conversion process can be described by the following equation:

[0124]

[0125] In the formula, η LDC,s The efficiency of the compressor under adiabatic conditions; h LDC Specific enthalpy (J / kg) of the compressor; p BOG,in and p BOG,out These are the volatile gas pressures (Pa) flowing into and out of the compressor, respectively; T BOG,out,s T represents the temperature (K) of the volatile gas exiting the compressor under isentropic conditions. BOG,in and T BOG,out These are the temperatures (K) of the volatile gas flowing into and out of the compressor, respectively.

[0126] Among these, component constraints also include pump set constraints, such as hot medium pumps (seawater), cold medium pumps (LNG), ORC working fluid circulation pumps, and LNG feed pumps.

[0127] Among them, for the thermal-hydraulic balance constraint: the pressure difference of heat exchangers, throttle valves and compressors is usually smaller than that of turbines and pumps, so the hydraulic losses of these components can be ignored in the overall system analysis.

[0128] The thermal-hydraulic balance constraints for the steam turbine are shown in the following equation:

[0129]

[0130] Where, m T-i Let k be the unit mass fluid flow rate (kg / s) passing through the i-th steam turbine; T k is the characteristic flow coefficient of the steam turbine. T-1 =1.85E-04, k T-2 =4.88E-04, k T-3 =6.9E-04, k T-4 =1.29E-03.

[0131] Secondly, considering the hydraulic constraints of the pump, the design operating conditions were simulated using Aspen HYSYSYS V11 and MATLAB software. The corresponding hydraulic performance parameters were obtained within a given evaporation temperature range. Finally, the data was fitted using a quadratic polynomial, yielding the following pump set constraints:

[0132]

[0133] In the formula, ρ is the density of the working fluid (kg / m³). 3 g is the acceleration due to gravity; p in and p out These represent the pressure (Pa) of the fluid flowing into and out of the pump unit, respectively; m cf denoted as , where is the mass of the circulating fluid (kg); a, b, and c are characteristic parameters.

[0134] Among them, the constraint on the type of working fluid can be derived from the basic thermodynamic state parameters based on the inherent functional relationship. The constraint model is shown below:

[0135] T pch,c =f(p pch ) = T pch,CON-i (p pch,CON-i );

[0136] T pch,e =f(p pch ) = T pch,VAP-i (p pch,VAP-i );

[0137] γ pch,c =g(p pch ) = h sat,v (ppch,CON-i )-h sat,w (p pch,CON-i );

[0138] γ pch,e =g(p pch ) = h sat,v (p pch,VAP-i )-h sat,w (p pch,VAP-i );

[0139] In the formula, T pch,c and T pch,e The phase change temperatures (K) of the condenser and evaporator are respectively; γ pch,c and γ pch,e The latent heat of phase change (J / kg) of the condenser and evaporator, respectively; p pch Pressure (Pa) during the phase change process; T pch,CON-i and T pch,VAP-i Let p be the phase change temperatures (K) of the i-th condenser and evaporator, respectively; pch,CON-i and p pch,VAP-i The phase change process pressures (Pa) of the i-th condenser and evaporator are respectively; h sat,v and h sat,w The values ​​are the specific enthalpy (J / kg) before and after the phase transition.

[0140] Given the pressure during the phase change process, the phase change temperature and latent heat of phase change, as fundamental thermophysical parameters, can be directly derived from the aforementioned intrinsic functional relationship.

[0141] Calculating the specific heat capacity of the working fluid requires considering three different scenarios:

[0142] (1) For organic working fluids in ORC systems, the assumption of constant specific heat capacity is usually adopted.

[0143] (2) For LNG, when the decision variable is temperature, the specific heat capacity of LNG is more sensitive to temperature changes. Because a quadratic polynomial fitting is required to obtain the LNG specific heat capacity values ​​for different decision variable ranges, the model is as follows:

[0144] c p (T)=aT 2 +bT+c;

[0145] Where a, b, and c are characteristic parameters; c p is the specific heat capacity (J / kg·K); T is the temperature (K).

[0146] (3) For other working fluids, the average specific heat capacity is used to calculate the specific heat capacity under different working conditions:

[0147]

[0148] In the formula, c p,eq ρ is the average specific heat capacity (J / kg·K); h is the specific enthalpy (J / kg); p in and p out These represent the gas pressure (Pa) at the inflow and outflow of the working fluid, respectively; T in and T out These represent the temperatures (K) of the working fluid as it flows in and out, respectively.

[0149] Step 5: Solve the heat flux topology model and comprehensively analyze the system performance (energy analysis, Analysis and heat transfer analysis of pyroclastic deposits, etc.

[0150] For energy analysis:

[0151] (1) Perform the analysis and solution of the system's net power generation. The model is as follows:

[0152]

[0153]

[0154]

[0155] In the formula, The system's power demand (J); The system's power generation (J); Energy consumed by the feed pump (J); Energy consumed by the seawater pump (J); and The energy consumed (J) by the first, second, third, and fourth drive pumps, respectively; and The energy (J) generated by the first, second, third, and fourth steam turbines, respectively; η P,s The efficiency of the pump set under isentropic conditions; Energy consumed by the compressor (J); η LDC,s The compressor efficiency is given by η under isentropic conditions. T For turbine efficiency; This represents the system's net power generation (J).

[0156] Under the carbon emission reduction target, the FSRU should generate more power than it needs.

[0157] (2) The consumption ratio (SDR) can reflect whether the FSRU system meets the carbon emission reduction standards and can obtain the proportional relationship between power generation and power demand. The model is as follows:

[0158]

[0159] (3) Thermal efficiency ηe The performance evaluation parameters measure the energy utilization efficiency during the thermal energy conversion process, and the model is as follows:

[0160]

[0161] In the formula, The input heat (J) to the system.

[0162] (4) The performance evaluation index of specific net power output (SNPO) can reflect the utilization of LNG's cold energy under different operating conditions. The model is as follows:

[0163]

[0164] In the formula, The flow rate per unit mass of LNG (kg / s).

[0165] Among them, for analyze: Often used to describe the availability of energy, power generation systems It includes both chemical and physical dissipation. However, the FSRU system studied in this example does not undergo any chemical reaction, therefore chemical dissipation is negligible, and the equation model only includes physical dissipation. The calculated dead zone states are 298.15 K and 101.325 kPa.

[0166]

[0167] e ph = (h-h0)-T0(s-s0);

[0168] In the formula, for (kW); The unit mass fluid flow rate (kg / s); e ph For the preheating process ratio (kJ / mol); h is the specific enthalpy (J / kg); h0 is the input specific enthalpy of the working fluid (J / kg); T0 is the initial temperature of the working fluid (K); s is the entropy (J / K); s0 is the initial value of the entropy (J / K).

[0169] For LNG cold Efficiency (η) ce This can be used to evaluate the degree of cold energy recovery in a system, and it can also reflect the heat exchange efficiency of the condenser. The model is as follows:

[0170]

[0171] In the formula, Useful (kW); For consumption (kW).

[0172] Regarding the heat transfer analysis of pyrophoric contamination: In the context of the FSRU system in this example, an assumption is made that the net pyrophoric contamination transfer during the system cycle is zero. Therefore, in a heat-to-work conversion dynamic system, heat-related pyrophoric contamination transfer is equivalent to work-related pyrophoric contamination transfer. Thus, the pyrophoric contamination dissipation transfer associated with accompanying heat in this system can be described by the following equation:

[0173]

[0174]

[0175] In the formula, and The heat transfer dissipation rates (W·K) of the evaporator and condenser are respectively. and Heat transfer associated with pyrolysis of seawater, circulating fluid, and LNG (W·K) are respectively.

[0176] The transfer of heat associated with net work can be calculated using the transfer of heat associated with accompanying heat, as shown in the following equation:

[0177]

[0178] In the formula, For useful fire accumulation transfer (W·K); and Heat transfer accompanying the fire in hot and cold states (W·K); and The values ​​are the heat transfer dissipation rates (W·K) under hot and cold conditions, respectively.

[0179] The above calculations lead to the system performance evaluation index (η). et It is defined as the ratio of the revenue from pyrotechnic transfer to the cost of pyrotechnic transfer:

[0180]

[0181] Step 6: Determine the objective function (e.g., thermodynamic functions such as efficiency, exergy, and dissipation), set the decision interval, and perform multi-objective optimization based on intelligent algorithms to solve for the optimal operating parameters of the system. In this example, the FSRU uses a pure organic working fluid in the three-stage ORC. The temperature range of the LNG regasification process is -162℃ to 10℃, and the boiling point of the organic working fluid should match the temperature range of the LNG regasification process. Therefore, a low-boiling-point organic working fluid needs to be selected as the circulating working fluid for the ORC. In this example, by searching relevant literature, six organic working fluids suitable for the three-stage ORC subsystem in the FSRU were obtained, including: Configuration A: R23; Configuration B: R143a; Configuration C: R290; Configuration D: R1234yf; Configuration E: R1234ze(E); Configuration F: R600a.

[0182] For the sake of simplicity in the analysis, several basic assumptions were adopted: (1) Steady-state conditions: The system operates under steady-state conditions, which facilitates consistency analysis; (2) Neglecting pipe effects: Compared with the pressure drop in the expander, the pressure drop and heat transfer effect in the connecting pipes can be ignored; (3) Constant specific heat capacity: It is assumed that the specific heat capacity of the organic mass in the single-phase heat exchanger remains constant; (4) Counter-current heat exchanger: Heat exchangers (such as high-temperature superheaters), evaporators and condensers are regarded as counter-current heat exchangers; (5) The components in the mixer are evenly distributed in the components, and the difference in fluid components is small, so the mass transfer rate is very small and can be ignored. The BOG reliquefaction process uses indirect heat exchange, avoiding direct spray cooling of the working fluid; (6) The pressure of NG is much lower than the critical pressure, and the compressibility factor can be regarded as 1, which is approximately an ideal gas; (7) As a compressible fluid, the specific heat capacity and density of LNG will change, but the heat exchangers in this example are divided into different stages according to the working quality state. In these stages, the changes in the specific heat capacity and density of LNG are relatively gradual. Therefore, the average value of the specific heat capacity of the inlet and outlet fluids in this stage can be used instead, and the density is the same. (8) For the thermal-hydraulic balance calculation of the pump, the process is approximated as an isothermal compression model. That is, when the temperature difference between the inlet and outlet of the pump is not large, the pressure rise of the pump can be approximated as an isothermal process, and the density of LNG is approximated as a constant value.

[0183] The mathematical model of the FSRU system is solved using a hierarchical and classification algorithm, and the system simulation process follows the algorithm flow:

[0184] (1) First, input the boundary and operating conditions to perform the simulation.

[0185] (2) Set initial values ​​for subsequent iterations, including the heat exchanger area: A CON-1,pch A CON-2,pch A CON-3,pch A CON-4,pch A VAP-1,pch AVAP-2,pch A VAP-3,pch A HEX1 A MX Working fluid mass flow rate: Power generated by the corresponding components:

[0186] (3) Calculate the heat transfer performance parameters related to the heat exchanger, such as the overall heat transfer coefficient and thermal conductivity.

[0187] (4) Calculate the relevant thermal resistance and thermodynamic potential based on the heat flow method.

[0188] (5) Solve the KCL and KVL equations using the matrix elimination method.

[0189] (6) Obtain the temperature and pressure at important nodes of the entire system.

[0190] (7) Update the assumed power of the corresponding components until convergence.

[0191] (8) Update the assumed value of the corresponding working fluid mass flow rate until convergence.

[0192] (9) After all the above assumptions converge, calculate the relevant performance evaluation parameters.

[0193] The comparison showed that all errors were within 5.6%. This result effectively verifies the reliability of the proposed global heat flux topology model.

[0194] Based on the above energy analysis, when the single decision variable is LNG pressure, the performance parameters of the FSRU system with six organic working fluid configurations were analyzed as the LNG pressure varied within the range of 6200 kPa to 6700 kPa. Configuration C had the highest net output power at 4.441 MW, with an average decrease rate of 26%. Therefore, the impact of LNG evaporation pressure changes on net power is not negligible. Under all configurations, the SDR (Self-Dryness Ratio) decreased with changing LNG evaporation pressure. Ensuring that the SDR for all configurations is greater than 1 allows the FSRU to achieve its carbon emission reduction target. Meanwhile, configuration C exhibited higher thermal efficiency compared to other configurations, reaching a maximum of 15.97% at the low pressure of 6200 kPa and a minimum of 11.25% at the highest pressure of 9200 kPa, a reduction of 29.56%. Furthermore, configuration C also had the highest SNPO (Synthetic Energy Per Utilization), reaching 28.99 kJ / kg at 6200 kPa and 21.46 kJ / kg at 9200 kPa, a reduction of 25.97%.

[0195] Based on the above energy analysis, when the single decision variable is seawater temperature, the performance parameters of the FSRU are analyzed as the seawater temperature varies between 15℃ and 35℃. Configuration C has a net output power of 4.19MW at a seawater temperature of 35℃ and 3.28MW at a seawater temperature of 15℃, with a change rate of 21.72%. The change in seawater temperature has a certain impact on the heat output of the evaporator, but the LNG evaporation pressure remains constant. The additional heat is converted into mechanical work through the turbine, generating energy transfer and increasing power generation. In this example, it is assumed that the turbine efficiency remains constant throughout the process; therefore, the trend of SDR change is the same as the trend of net output power change. Furthermore, configuration C exhibits the highest thermal efficiency, reaching 21.73% at a seawater temperature of 35℃ and 11.1% at a seawater temperature of 15℃, resulting in a change rate of 48.9%. It is evident that seawater temperature (as the heat source temperature) has a significant impact on the system's thermal efficiency. Meanwhile, configuration C also exhibited the highest SNPO, reaching 21.41 kJ / kg at a seawater temperature of 15°C and 27.35 kJ / kg at a seawater temperature of 35°C, with a change rate of 21.71%. Therefore, the effects of seawater temperature and LNG pressure on SNPO are relatively consistent.

[0196] Based on the above energy analysis, when the single decision variable is evaporation temperature, the performance parameters of the FSRU are analyzed as the evaporation temperature varies within the range of 5°C to 10°C. Configuration C achieves the highest net power output, reaching 3.72 MW at both evaporator temperatures of 5°C and 10°C. Since the impact of evaporator temperature on power generation and consumption remains relatively consistent, the SDR (Survey Rate of Return) change is not significant. Simultaneously, configuration C maintains a high thermal efficiency, reaching a maximum of 15.06% at an evaporator temperature of 6°C. The impact of evaporation temperature on LNG flow rate is relatively small compared to its impact on net power output; under these conditions, configuration C achieves the highest SNPO (Survey Rate of Return).

[0197] Based on the above Analysis and heat transfer analysis of pyroclastic deposits, with LNG pressure as the single decision variable: cold Efficiency increases with LNG evaporation pressure. Configuration C achieves maximum cooling efficiency at the highest pressure of 9200 kPa. The efficiency is 57.68%. The ignition transfer efficiency decreases with changing LNG evaporation pressure. Configuration E has the highest ignition transfer efficiency, reaching 31.34% at a low pressure of 6200 kPa and 30.24% at a high pressure of 9200 kPa, a decrease of 3.5%. Compared with performance parameters such as net output power, the rate of change in ignition transfer efficiency is relatively low; therefore, LNG pressure has a relatively small impact on ignition transfer efficiency. If net output power and cooling... Efficiency is the evaluation metric, and the optimal configuration remains C.

[0198] Based on the above Analysis and pyroclastic heat transfer analysis, with seawater temperature as the single decision variable: cold Efficiency decreases slightly with changes in seawater temperature. (Configuration C: Cooling) The system achieves its highest efficiency, reaching 57.71% at seawater temperatures of 15°C and 56.75% at 35°C, with a change rate of only 1.7%. Therefore, seawater temperature has a significant impact on the system's cooling efficiency. The impact on efficiency is very limited. Heat transfer efficiency increases with seawater temperature. Configuration E has the highest heat transfer efficiency, reaching 30% at 15°C and 32.45% at 35°C, a decrease of 7.6%. Similar to the previous cases, evaluating the overall system heat transfer capacity leads to changes in the optimal configuration. Therefore, when selecting the optimal configuration affected by seawater temperature based on net output power and condenser efficiency, configuration C remains the best choice.

[0199] Based on the above Analysis and heat transfer analysis of pyroclastic deposits, with the single decision variable being evaporation temperature: cold Efficiency shows a slight increasing trend with changes in evaporation temperature. LNG cooling system with configuration C... The highest efficiency is achieved at an evaporation temperature of 5°C (57.46%) and 10°C (58.48%), with a minimum change rate of only 1.74%. Therefore, evaporator temperature significantly affects LNG cooling efficiency. The impact on efficiency is very limited. The pyrophoric transfer efficiency increases with evaporation temperature. Configuration A has the highest pyrophoric transfer efficiency, reaching 24.12% at evaporator temperature of 5°C and 29.49% at 10°C, with a decrease rate of 18.2%. Therefore, the impact of evaporation temperature on the overall pyrophoric transfer efficiency cannot be ignored. Similar to the previous situation, the assessment of overall heat transfer capacity leads to changes in the optimal configuration. However, considering net output power and LNG cooling... When efficiency is the evaluation criterion, configuration C is still the optimal choice.

[0200] When the two decision variables are LNG pressure and seawater temperature: Under conditions of low LNG pressure and high seawater temperature, it is easier to obtain higher net power output, and the system effect of LNG pressure and seawater temperature is significant. Under conditions of higher LNG pressure and lower seawater temperature, it is easier to obtain higher LNG cooling capacity. Efficiency. Under low LNG pressure and low seawater temperature conditions, it is easier to obtain higher pyrophoretic transfer efficiency, and the synergistic impact of changes in LNG pressure and seawater temperature on system performance parameters is relatively small.

[0201] When the two decision variables are LNG pressure and evaporation temperature: At lower LNG pressures, higher net power output is more easily achieved, but the impact of evaporation temperature is smaller; in this case, LNG pressure and evaporator temperature have a significant synergistic effect. At lower LNG pressures, higher SDR is more easily achieved, while evaporation temperature still has a smaller impact; in this case, the synergistic effect weakens. Under low LNG pressure and high evaporation temperature conditions, higher thermal efficiency is more easily achieved, but the synergistic effect between the two is limited. At high LNG pressure and high evaporation temperature, higher LNG cooling efficiency is more easily achieved. While both LNG pressure and evaporator temperature have varying effects on efficiency, the influence of LNG pressure is significantly greater than that of evaporation temperature, exhibiting a strong synergistic effect. Higher ignition transfer efficiency is more easily achieved under low LNG pressure and low evaporator temperature conditions, but the synergistic effect between the two variables is not significant.

[0202] In this study, a multi-objective optimization method was used to select two performance parameters that describe the overall system performance: thermal efficiency and pyrophoresis transfer efficiency. However, these two performance parameters conflict within the range of decision variables, requiring a trade-off. To achieve this balance, a genetic algorithm was used to optimize the two objectives of the system, yielding the Pareto front. Subsequently, the TOPSIS multi-criteria decision method was applied to select the optimal operating point. Considering that the evaporator simulation was conducted under a single decision variable, this example performed multi-objective optimization for different evaporation temperatures and optimized the system performance parameters for different seawater temperatures. The variations in the optimal operating point under different evaporator temperatures and different seawater temperatures were obtained. By using the above methods, this example successfully achieved a comprehensive evaluation of the system performance under different operating conditions and obtained the optimal operating point considering the trade-off between thermal efficiency and pyrophoresis transfer efficiency.

[0203] Step 7, End.

[0204] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A global heat flux topology modeling and optimization method for the cold energy recovery and regasification process of a large-scale floating storage and regasification unit, characterized in that, include: Step 1: Based on the energy conservation of each component of the system and the inlet and outlet conditions, compare the heat flow topology and establish the first model, which is the preliminary heat flow topology model; Step 2: Combining the first model with Kirchhoff's laws and the thermocurrent method, eliminate redundant heat flow loops and establish the second model, which is the final global heat flow topology model. Step 3: Establish the third model, which is used to determine the heat flow balance, thermal-hydraulic balance, working fluid type, system operating conditions, and various constraints of the coupling links of each system. Step 4: Based on the second and third models, solve the heat flow topology model and comprehensively analyze the system performance; the analysis of system performance should include at least energy analysis, heat transfer analysis and heat transfer analysis. Step 5: Based on Step 4, determine the objective function, set the decision interval, and perform multi-objective optimization based on intelligent algorithms to solve for the optimal operating parameters of the system; the objective function includes at least efficiency, efficiency, and heat dissipation mechanical functions. Step Six: Introduce an FSRU system instance and analyze the methods in Steps One through Five to verify the accuracy of the instance calculation results; In step one, the modeling method for the preliminary heat flow topology model includes: S1. Based on the comprehensive global heat flow topology model of a large-scale thermal system, the basic components of the thermal system, namely heat exchangers, evaporators, condensers, mixers, and recondensers, are equivalent to heat flow models that include thermal resistance. Among them, the heat flow model that includes thermal resistance has a unique configuration based on the fact that three fluids flow through the recondenser. S2. Based on the comprehensive global heat flow topology model of a large-scale thermal system, the basic components of the drive system, namely the throttle valve, drive pump, compressor, turbine and separator, are equivalent to a heat flow model that includes thermodynamic potential. S3. By combining the component models in S1 and S2 and describing the system's heat flow topology, a preliminary global heat flow topology model is established, namely, the preliminary heat flow topology model. In step two, the final global heat flow topology model is established as follows: When constructing the heat flow model, based on Kirchhoff's laws and the thermocurrent method, the closed loop consisting only of thermodynamic potential represents the temperature change in the closed loop and is eliminated without changing the basic properties of the system, thus establishing the final global heat flow topology model.

2. The global heat flux topology modeling and optimization method for cold energy recovery and regasification processes in large-scale floating storage and regasification devices according to claim 1, characterized in that, In step three, the heat flow balance constraints include energy flow topology constraints, energy conservation constraints, energy flow transfer and conversion constraints, and component constraints. Among them, the energy conservation equation describing the heat-work conversion process of the entire system is obtained by using the KCL equations; the heat transfer and conversion within the system is reflected by using the heat flow method through the KVL equations; the component constraints include heat exchanger constraints, turbine constraints, compressor constraints, and pump set constraints. The heat exchanger constraints are: based on the thermal resistance and thermodynamic potential of the heat flow method, the heat exchanger under the design conditions is simulated by combining Aspen HYSYSYS V11 software and MATLAB software to obtain the heat exchange performance parameters. The turbine constraints are constraint equations describing the heat-work conversion process in the turbine. Since there are irreversible losses caused by irreversible factors in each process of the actual turbine cycle, the influence of irreversible factors on cycle performance is considered, which is the turbine element constraint.

3. The global heat flux topology modeling and optimization method for the cold energy recovery and regasification process of a large-scale floating storage and regasification unit according to claim 1 or 2, characterized in that, In step three, the thermal-hydraulic balance constraints include constraints on the turbine and the pump; the thermal-hydraulic balance constraints of the pump are obtained by the following method: the pump is simulated under design conditions using Aspen HYSYSYSV11 software and MATLAB software, and the corresponding thermal-hydraulic performance parameters are obtained for a given evaporation temperature range. Finally, the parameters are fitted with a quadratic polynomial to obtain the thermal-hydraulic balance constraints of the pump. The working fluid type constraint refers to the analysis of the working fluid's thermophysical properties. These properties are derived from fundamental thermodynamic state parameters based on inherent functional relationships. Specifically, when the pressure during the phase change process is known, the phase change temperature and latent heat of phase change are used as fundamental thermophysical parameters and directly derived from these inherent functional relationships. Three cases are considered for the specific heat capacity of the working fluid: for organic working fluids in ORC systems, a constant specific heat capacity assumption is adopted; for LNG, since its specific heat capacity is more sensitive to temperature changes, a quadratic polynomial fitting is used to obtain specific heat capacity values ​​for different decision variable ranges; for other working fluids, the average specific heat capacity is used to calculate the specific heat capacity under different operating conditions.

4. The global heat flux topology modeling and optimization method for the cold energy recovery and regasification process of a large-scale floating storage and regasification unit according to claim 1, characterized in that, In step four, the energy analysis includes solving for net power generation, system consumption ratio, system thermal efficiency, and specific net output power. First, net power generation is calculated to ensure that system power generation exceeds power consumption to meet emission reduction targets. Then, the system consumption ratio is calculated based on net power generation to obtain the proportional relationship between system power generation and demand. Next, system thermal efficiency is calculated based on net power generation to determine the energy utilization efficiency during the system's thermal energy conversion process. Finally, specific net output power is calculated based on net power generation to reflect the utilization of LNG cold energy under different operating conditions. The analysis includes solving for the LNG cooling efficiency, which is used to evaluate the degree of cold energy recovery of the system and the heat exchange efficiency of the condenser; In the analysis of heat transfer in the fire accumulation zone, the associated work, associated heat, associated heat dissipation, and net fire accumulation transfer in the cycle are redefined, thereby leading to the system performance evaluation index, namely the ratio of fire accumulation transfer benefit to fire accumulation transfer cost.

5. The global heat flux topology modeling and optimization method for the cold energy recovery and regasification process of a large-scale floating storage and regasification unit according to claim 1, characterized in that, In step five, based on step four, the most efficient system configuration is obtained by analyzing the changes in system performance parameters under the influence of a single decision variable; whereby the single decision variable includes LNG pressure, seawater temperature, and evaporation temperature.

6. The global heat flux topology modeling and optimization method for the cold energy recovery and regasification process of a large-scale floating storage and regasification unit according to claim 5, characterized in that, In step five, the system performance parameters under the influence of two decision variables are analyzed based on the most efficient system configuration to obtain the changes in the performance parameters of the selected system configuration under the synergistic influence of the two decision variables; among them, the two decision variables include LNG pressure and seawater temperature, as well as LNG pressure and evaporation temperature.

7. The global heat flux topology modeling and optimization method for the cold energy recovery and regasification process of a large-scale floating storage and regasification unit according to claim 6, characterized in that, In step five, a multi-objective optimization method is used to select two parameters describing the overall system performance: thermal efficiency and pyroelectric transfer efficiency. Thermal efficiency represents the system's ability to convert thermal energy into mechanical work, while pyroelectric transfer efficiency characterizes the system's heat transfer capacity. Within the range of decision variables, the two performance parameters, thermal efficiency and pyroelectric transfer efficiency, conflict with each other. To balance thermal efficiency and pyroelectric transfer efficiency, a genetic algorithm is used to optimize the two objectives of the system and obtain the Pareto front. Then, the TOPSIS multi-criteria decision method is applied to perform multi-objective optimization for different evaporator temperatures and optimize the system performance parameters for different seawater temperatures within the decision interval, thereby obtaining the optimal operating point.

8. The global heat flux topology modeling and optimization method for cold energy recovery and regasification processes in large-scale floating storage and regasification units according to claim 7, characterized in that, In step six, the FSRU case is used, combined with Aspen HYSYS V11 software to simulate component parameters, to verify the modeling and simulation results: First, six different organic working fluid configurations were selected to evaluate the overall performance of large-scale LNG cold energy recovery and regasification in FSRUs. The most suitable organic working fluid configuration was determined by analyzing the system performance parameters under the influence of a single decision variable. A bubble diagram of the overall thermal resistance distribution of the FSRU was plotted to represent the change in total thermal resistance of the optimal configuration under different operating conditions. Secondly, an analysis of the system performance parameters under the influence of two decision variables is conducted for this configuration; Finally, a multi-objective optimization method was used to select two parameters describing the overall performance of the system: thermal efficiency and pyrolysis transfer efficiency. A genetic algorithm was used to balance the two parameters, and then the TOPSIS multi-criteria decision method was used to obtain the optimal operating point. After comparison, all errors were within 5.6%, thus confirming the accuracy and reliability of the system model.

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