A dynamic analysis and control method for offshore LNG ship-to-ship bunkering process
By combining real-time data acquisition and dynamic numerical analysis models with customized control strategies, the problems of low efficiency, high cargo damage, and high safety risks in LNG ship-to-ship bunkering operations have been solved, achieving precise, safe, and efficient bunkering control.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- 中海油能源发展股份有限公司采油服务分公司
- Filing Date
- 2026-05-08
- Publication Date
- 2026-06-05
AI Technical Summary
Existing LNG carrier-to-ship bunkering operations rely on experience, resulting in low bunkering efficiency, high cargo damage, and significant safety risks. They also cannot adapt to the differences in cabin type and capacity of different recipient vessels and lack real-time data analysis and control methods.
By acquiring real-time data and performing standardized preprocessing, a dynamic numerical analysis model is constructed. Combined with a customized model and dynamic control strategy, a refueling rate control command is generated to achieve closed-loop optimized control of the LNG refueling process.
It improves refueling efficiency, reduces cargo damage rate and safety risks, adapts to the differences in cabin type and capacity of different refueling vessels, and achieves precise, safe and efficient refueling control.
Smart Images
Figure CN122153350A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of LNG carrier bunkering technology, and in particular to a dynamic analysis and control method for the offshore LNG carrier-to-ship bunkering process. Background Technology
[0002] As a clean and low-carbon energy source, LNG's demand for maritime transport and bunkering continues to grow, making LNG ship-to-ship bunkering technology a crucial link in the LNG industry chain. Currently, existing LNG ship-to-ship bunkering operations mainly rely on the operational experience of crew members, lacking dynamic analysis and precise control methods based on actual ship data, resulting in the following core technical problems: (1) Low refueling efficiency: The crew members carry out LNG ship-to-ship refueling operations based on experience, which cannot match the real-time status of the cargo tank of the receiving ship (such as initial tank pressure and liquid level), which easily leads to problems such as "the refueling rate is too high, causing the tank pressure of the receiving ship to rise sharply" or "the refueling rate is too low, prolonging the operation time". (2) Difficulty in controlling cargo loss: During the LNG refueling process, evaporation gas (BOG) will be generated due to environmental heat exchange and flow disturbance. The existing operation lacks real-time prediction and control of BOG generation, resulting in BOG not being recovered in time and some BOG being directly emitted, causing cargo loss. (3) High safety risks: The experience-based operation method cannot predict abnormal changes in tank pressure and temperature in advance. When sudden working conditions occur (such as a sudden rise in ambient temperature or pipeline blockage), the crew's response is delayed, which can easily lead to safety hazards such as cargo tank overpressure and LNG leakage. (4) Poor adaptability: Different injection vessels have different tank types (B type, membrane type, C type), capacities, and design pressures. Existing general operating procedures cannot adapt to customized needs, further exacerbating efficiency and safety issues.
[0003] Therefore, there is an urgent need for a dynamic and controllable LNG bunkering operation guidance scheme based on real ship data to address the shortcomings of existing technologies, such as reliance on experience, low efficiency, high cargo damage, and high safety risks. Summary of the Invention
[0004] This invention aims to solve at least one of the technical problems existing in related technologies. To this end, this invention provides a dynamic analysis and control method for the ship-to-ship LNG bunkering process at sea, which overcomes the shortcomings of existing LNG bunkering methods, such as reliance on experience, low efficiency, high cargo loss, and significant safety risks, thereby improving the efficiency and safety of LNG bunkering.
[0005] This invention provides a method for dynamic analysis and control of the LNG ship-to-ship bunkering process at sea, comprising the following steps: S1, the data acquisition module collects the basic refueling parameters of the refueling vessel in real time, the data acquisition module obtains the information of the receiving vessel, and the data processing module performs data preprocessing on the basic refueling parameters and the information of the receiving vessel to obtain a standardized dataset; S2, Construct a dynamic numerical analysis model to characterize the LNG refueling process; S3, Match the dynamic numerical analysis model, the standardized dataset, and the historical working condition data to correct the dynamic numerical analysis model into a customized model; S4, set a dynamic control strategy, combine the customized model with the dynamic control strategy to generate a refueling rate control command, and the refueling vessel controls the LNG refueling operation according to the refueling rate control command; S5, determine whether the injection rate after adjustment in step S4 is the optimal injection rate; If the result is negative, return to step S4; If the judgment result is yes, the bunkering vessel will continue bunkering until the LNG bunkering operation is completed.
[0006] A further improvement of the present invention on the dynamic analysis and control method of LNG ship-to-ship bunkering process is that the basic bunkering parameters include cargo hold temperature, cargo hold pressure, cargo hold density, cargo hold liquid level, LNG bunkering rate, LNG flow rate, and pump speed.
[0007] A further improvement of the present invention on the dynamic analysis and control method of LNG ship-to-ship bunkering process at sea is that the data preprocessing in step S1 includes the following steps: When the pump power of the refueling vessel reaches its maximum, it is the full-speed stage; as the pump power gradually increases, it is the acceleration stage. The data processing module acquires basic refueling parameters and recipient vessel information. After the refueling vessel begins refueling LNG at a set time, the data processing module checks the basic refueling parameters according to a threshold judgment formula. The threshold judgment formula is as follows: in, Threshold for LNG flow rate determination For LNG flow rate, This represents the average flow rate monitored for LNG. The cargo hold temperature threshold. For cargo hold temperature, This represents the average temperature monitored in the cargo hold. This represents the temperature standard deviation. This refers to the pump speed; The data processing module performs a data removal operation to remove basic annotation parameters that do not conform to the threshold judgment formula.
[0008] A further improvement of the present invention on the dynamic analysis and control method of LNG ship-to-ship bunkering process is that, when in the full-speed stage, the data processing module performs a data rejection operation every 1 second; when in the acceleration stage, the data processing module performs a data rejection operation every 10 seconds.
[0009] A further improvement of the present invention regarding the dynamic analysis and control method for LNG ship-to-ship bunkering process at sea is that step S2 includes: The dynamic numerical analysis model is as follows: in, To generate quality for BOG, Let be the heat transfer coefficient of the liquid phase. Where is the heat transfer coefficient of the gas phase. Gas-liquid interface area For the enthalpy of the liquid phase, Enthalpy in the gas phase, The first scaling factor, The second scaling factor, For interface temperature, The temperature of LNG, For the temperature of BOG, For the heat transfer from the interface to the liquid phase, This refers to the heat transfer from the interface to the gas phase.
[0010] A further improvement of the present invention on the dynamic analysis and control method of LNG ship-to-ship bunkering process is that the historical operating data includes the cargo hold type of the receiving ship, the amount of bunkering, the temperature inside the cargo hold of the receiving ship, and the initial tank pressure of the receiving ship.
[0011] A further improvement of the present invention regarding the dynamic analysis and control method for LNG carrier-to-ship bunkering at sea lies in that, when the cargo hold type of the receiving vessel is C-type, the interface is in thermal equilibrium, the temperature variation range within the cargo hold of the receiving vessel is -150℃ to -120℃, and the tank pressure of the receiving vessel is 0.1MPa to 0.5MPa, The customized model is as follows: in, This is the saturated steam temperature.
[0012] A further improvement of the present invention on the dynamic analysis and control method for LNG ship-to-ship bunkering process at sea lies in that the dynamic control strategy includes the following steps: The customized model analyzes the current BOG generation, cargo hold temperature, and cargo hold pressure, and predicts the changing trends of BOG generation, cargo hold temperature, and cargo hold pressure within a set time period. Based on the changing trends, it is determined whether the BOG generation or cargo hold pressure exceeds the standard. If the result is negative, maintain the current betting rate; If the judgment result is yes, an alarm command is triggered and a control command is output. After control, the BOG generation or cargo hold pressure is re-judged to see if it exceeds the standard. The control instructions are as follows: in, For comprehensive correction factors, The maximum allowable processing quality for BOG is preset. The current LNG refueling flow rate is collected by the data acquisition module. The LNG refueling flow rate is to be adjusted.
[0013] A further improvement of the present invention regarding the dynamic analysis and control method for LNG ship-to-ship bunkering at sea lies in the fact that the calculation formula for BOG generation during the bunkering process, based on a customized model, is as follows: in, For cargo hold heat exchange area, Due to the temperature difference between the inside and outside of the cargo hold, The heat transfer coefficient is... As latent heat of vaporization, To extend the duration, For BOG generation quantity; The formula for calculating cargo hold pressure during the refueling process, based on a customized model, is as follows: in, For cargo hold pressure, Initial chamber pressure, This refers to BOG emissions. For cargo hold gas phase space volume, For LNG molar mass, is the gas constant.
[0014] A further improvement of the present invention on the dynamic analysis and control method of LNG ship-to-ship bunkering process is that the data acquisition module includes a sensor network, and the sensor network is arranged in the following locations: the outlet of the bunkering ship's output pipeline, the LNG transmission pipeline, the LNG cargo tank of the bunkering ship, and the liquid dome area.
[0015] This invention presents a dynamic analysis and control method for the offshore LNG carrier-to-ship bunkering process. It constructs a fully intelligent management and control system encompassing data preprocessing, dynamic modeling, operational condition correction, dynamic regulation, and closed-loop optimization, achieving precise, safe, and efficient control of the bunkering process. Step S1, through real-time data acquisition and standardized preprocessing, solves the problems of non-standard data acquisition, outlier interference, and inconsistencies between multi-source data in traditional operations. It simultaneously collects basic bunkering parameters of the bunkering vessel and information of the receiving vessel, covering core variables throughout the bunkering process and avoiding data omissions. The data processing module performs preprocessing such as outlier removal and data smoothing, generating a standardized dataset. This eliminates the interference of data fluctuations and noise on subsequent modeling and regulation, significantly improving the robustness of the entire bunkering system and providing reliable data support for subsequent accurate modeling and dynamic regulation. Steps S2 and S3, through dynamic model construction and operational condition matching and correction, solve the problems of traditional methods relying on crew experience and having poor adaptability. A general dynamic numerical analysis model characterizing the LNG refueling process is constructed. Based on the physical principles of gas-liquid phase change and the law of heat transfer conservation, core processes such as BOG generation and cargo hold temperature and pressure changes during refueling are quantified. This achieves calculable and predictable refueling status at the principle level, completely eliminating reliance on human experience. By matching and correcting the model with standardized datasets and historical operational condition data, a customized model adapted to the current refueling scenario is generated. This model can adapt to different refueling vessel hold types, refueling volumes, initial cargo hold states, and other varying operational conditions. The model's prediction error is controllable, significantly improving model accuracy and scenario adaptability. Steps S4 and S5, through dynamic control strategies and closed-loop optimization, solve the problems of balancing safety and efficiency and high cargo damage rates in traditional operations.
[0016] This invention uses a customized model to calculate BOG generation and cargo hold temperature and pressure trends in real time. Combined with a dynamic control strategy centered on BOG handling capacity, it generates control commands to stably control BOG generation and maintain tank pressure within the safe threshold of the receiving vessel's design pressure. This proactively avoids safety risks such as tank pressure overpressure and BOG exceeding limits, significantly reducing the probability of safety accidents such as overpressure and leakage. Through a closed-loop judgment process, the refueling rate is continuously verified. If the optimal rate is not reached, it is cyclically adjusted until the optimal refueling rate is locked, maximizing refueling efficiency under the premise of safety. This avoids the extreme situations in traditional manual operations where excessive speed reduction is used to ensure safety, or safety is ignored in order to improve efficiency. The refueling operation time can be significantly shortened, and the refueling efficiency can be greatly improved. Precise control of BOG generation reduces cargo loss caused by direct BOG emissions, reducing the LNG cargo loss rate to an extremely low level and significantly improving the economics of refueling operations.
[0017] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description
[0018] To more clearly illustrate the technical solutions in this 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 this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.
[0019] Figure 1 This is a schematic diagram of a dynamic analysis and control method for the LNG ship-to-ship bunkering process provided by the present invention.
[0020] Figure 2 This is a schematic diagram of the data removal operation in a dynamic analysis and control method for LNG ship-to-ship bunkering process provided by the present invention.
[0021] Figure 3 This is a schematic diagram of flow rate iteration optimization in a dynamic analysis and control method for LNG ship-to-ship bunkering process provided by the present invention.
[0022] Figure 4 It is a graph showing the change of saturated steam temperature with pressure.
[0023] Figure 5 This is a schematic diagram of the pump curve. Figure 1 .
[0024] Figure 6 This is a schematic diagram of the pump curve. Figure 2 .
[0025] Figure 7 This is a schematic diagram of the pump curve. Figure 3 . Detailed Implementation
[0026] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention. The following embodiments are used to illustrate this invention but should not be used to limit the scope of this invention.
[0027] The following is combined with Figures 1 to 3 The present invention describes a method for dynamic analysis and control of LNG ship-to-ship bunkering process at sea, comprising the following steps: S1, the data acquisition module collects the basic refueling parameters of the refueling vessel in real time, the data acquisition module obtains the information of the receiving vessel, and the data processing module performs data preprocessing on the basic refueling parameters and the information of the receiving vessel to obtain a standardized dataset; S2, Construct a dynamic numerical analysis model to characterize the LNG refueling process; S3, Match the dynamic numerical analysis model, the standardized dataset, and the historical working condition data to correct the dynamic numerical analysis model into a customized model; S4, set a dynamic control strategy, combine the customized model with the dynamic control strategy to generate a refueling rate control command, and the refueling vessel controls the LNG refueling operation according to the refueling rate control command; S5, determine whether the injection rate after adjustment in step S4 is the optimal injection rate; If the result is negative, return to step S4; If the judgment result is yes, the bunkering vessel will continue bunkering until the LNG bunkering operation is completed.
[0028] Furthermore, the basic refueling parameters include cargo hold temperature, cargo hold pressure, cargo hold density, cargo hold liquid level, LNG refueling rate, LNG flow rate, and pump speed.
[0029] Furthermore, the data preprocessing in step S1 includes the following steps: When the pump power of the refueling vessel reaches its maximum, it is the full-speed stage; as the pump power gradually increases, it is the acceleration stage. like Figure 2 As shown, the data processing module acquires basic refueling parameters and recipient vessel information. After the refueling vessel begins refueling LNG at the set time, the data processing module checks the basic refueling parameters according to a threshold judgment formula. The threshold judgment formula is: in, Threshold for LNG flow rate determination For LNG flow rate, This represents the average flow rate monitored for LNG. The cargo hold temperature threshold. For cargo hold temperature, This represents the average temperature monitored in the cargo hold. This represents the temperature standard deviation. This refers to the pump speed; The data processing module performs a data removal operation to remove basic annotation parameters that do not conform to the threshold judgment formula.
[0030] Specifically, when in the full-speed phase, the data processing module performs a data removal operation every 1 second; when in the acceleration phase, the data processing module performs a data removal operation every 10 seconds.
[0031] Ideally, due to the high frequency and massive volume of monitoring data, eliminating data one by one would be computationally intensive and time-consuming. Because the data changes rapidly during the acceleration process, a high-frequency check is used, i.e., data is extracted once per second. During the full-speed phase, flow fluctuations are smaller, and overall data changes are minimal; therefore, abnormal data is extracted every 10 seconds and compared with the data from the previous step to determine the final data.
[0032] Preferably, the data processing module uses the Z-score method for data preprocessing to obtain a standardized dataset; the Z-score method is used to check the data one by one and remove outliers based on the average value of the monitored data.
[0033] In one implementation case, such as Figure 5 , Figure 6 and Figure 7 As shown, under full flow rate conditions, the flow rate judgment threshold is set to... However, the flow rate changes significantly during acceleration, making it impossible to directly use the Z-score method for data removal. The flow rate fluctuates greatly during the increase, making it impossible to use the Z-score method directly for threshold removal. Since only pump speed can be adjusted and monitored on-site, this invention incorporates pump speed data into the flow rate anomaly data removal process. The rated speed and flow rate of the submersible pump have a linear relationship; under rated operating conditions, a relationship of Qn at a specific head can be fitted as Q=0.0072n. Based on the relationship between pump flow rate and power, it can be analyzed that the flow rate increases with increasing speed, but at the same speed, the flow rate changes with variations in power and efficiency, increasing the difficulty of selecting a threshold for anomaly data. To fully consider the impact of flow rate, efficiency, and power on the data, and to accurately extract flow rate data, efficiency and power are substituted into the threshold judgment formula, replacing the monitored average value, based on... After conversion ,in, This refers to the pump flow rate (which can be converted to LNG flow rate). For power, For efficiency, For density, It is the acceleration due to gravity. For head. Power at rated speed. threshold Its efficiency threshold The final flow threshold for the acceleration process was determined. This invention uses pump speed as a data rejection parameter, while also considering efficiency and power as threshold criteria, effectively addressing the issue of large and drastic fluctuations in data over time. By employing a phased data rejection method, outlier values can be quickly and accurately removed at each stage, preserving accurate data.
[0034] Further, step S2 includes: The dynamic numerical analysis model is as follows: in, To generate quality for BOG, Let be the heat transfer coefficient of the liquid phase. Where is the heat transfer coefficient of the gas phase. Gas-liquid interface area For the enthalpy of the liquid phase, Enthalpy in the gas phase, The first scaling factor, The second scaling factor, For interface temperature, The temperature of LNG, For the temperature of BOG, For the heat transfer from the interface to the liquid phase, This refers to the heat transfer from the interface to the gas phase.
[0035] Furthermore, the historical operating condition data includes the cargo hold type of the receiving vessel, the amount of fuel added, the temperature inside the cargo hold of the receiving vessel, and the initial tank pressure of the receiving vessel.
[0036] Preferably, the cargo hold type can be B, membrane, or C.
[0037] Furthermore, the dynamic numerical analysis model was compared with the matching working conditions in the historical working condition data, and the scaling factor in the dynamic numerical analysis model was corrected; based on the changes in temperature, pressure and liquid level inside the receiving chamber at the refueling site, the following targeted assumptions were made regarding the heat transfer at the gas-liquid interface: (1) Since the liquid enters from the bottom during the refueling process, the interface has little impact on most of the refueling time. The gas-liquid interface follows the interface heat balance assumption during the refueling process, and there is no efficiency loss. and All can be taken as 1; (2) Due to the temperature change inside the receiving ship's cargo hold and the pressure change inside the C-type tank, according to the on-site monitoring data: the temperature change range inside the receiving ship's cargo hold is -150℃~-120℃, and the pressure inside the receiving tank is 0.1MPa~0.5MPa. According to the corresponding historical working condition data, the interface temperature will change with the pressure and is difficult to detect. Since the interface temperature is close to the saturated steam temperature, the model interface temperature uses a constant value, which cannot guarantee accuracy. Therefore, this model uses the saturated steam temperature that changes with the pressure (e.g. Figure 4 (as shown); (3) The gas-liquid interface inside the LNG cargo tank cannot store heat and mass. Macroscopically, the overall interface is in a state of energy balance, so the interface ; Based on the above three assumptions, a customized model for calculating the mass rate of the gas-liquid interface BOG (Bottle-Off Gas) is finally obtained to adapt to the on-site refueling scenario. in, This is the saturated steam temperature.
[0038] Furthermore, such as Figure 1 and Figure 3 As shown, the dynamic control strategy includes the following steps: The customized model analyzes the current BOG generation, cargo hold temperature, and cargo hold pressure, and predicts the changing trends of BOG generation, cargo hold temperature, and cargo hold pressure within a set time period. Based on the changing trends, it is determined whether the BOG generation or cargo hold pressure exceeds the standard. If the result is negative, maintain the current betting rate; If the judgment result is yes, an alarm command is triggered and a control command is output. After control, the BOG generation or cargo hold pressure is re-judged to see if it exceeds the standard. The control instructions are as follows: in, For comprehensive correction factors, The maximum allowable processing quality for BOG is preset. The current LNG refueling flow rate is collected by the data acquisition module. The LNG refueling flow rate is to be adjusted.
[0039] Furthermore, based on the customized model, the formula for calculating the BOG generation during the refueling process is as follows: in, For cargo hold heat exchange area, Due to the temperature difference between the inside and outside of the cargo hold, The heat transfer coefficient is... As latent heat of vaporization, To extend the duration, For BOG generation quantity; The formula for calculating cargo hold pressure during the refueling process, based on a customized model, is as follows: in, For cargo hold pressure, Initial chamber pressure, This refers to BOG emissions. For cargo hold gas phase space volume, For LNG molar mass, is the gas constant.
[0040] The historical operating condition data update mechanism is as follows: after each refueling operation is completed, the key parameters, model correction coefficients, and control effect data (refueling efficiency, cargo damage rate) of this operation are entered into the database, and the operating condition classification boundary is updated through clustering algorithm.
[0041] When the customized model predicts that the BOG generation exceeds a preset threshold (3% of the current refueling volume), the control command prioritizes reducing the refueling rate while increasing the processing capacity of the reliquefaction unit or gas incineration unit. When the predicted cargo tank pressure exceeds a safety limit (90% of the design pressure of the cargo tank of the receiving vessel), an alarm command is triggered, which is an audible and visual alarm and an emergency deceleration command is output.
[0042] During the execution of the dynamic control strategy, based on the calculation results, if the BOG production is normal (≤3% of the current refueling volume) and the cargo hold pressure is stable (≤80% of the design pressure), the command "maintain the current refueling rate" is output; if the BOG production exceeds the standard, a combination command "reduce the refueling rate by 5%~10% and increase the amount of BOG sent to the gas combustion unit by 20%" is output; if the cargo hold pressure is close to the safety limit (>85% of the design pressure), the command "emergency speed reduction by 20% and open the backup pressure relief valve" is output, and an audible and visual alarm is triggered at the same time.
[0043] In the dynamic control strategy, an auxiliary decision-making report is generated in real time, which includes "current operating condition safety score (1-10 points), BOG cargo loss prediction value (kg), optimal refueling time recommendation (h), and potential risk points (such as high pipeline pressure)". The report is then synchronized to the crew's control panel via a display screen to assist the crew in making decisions.
[0044] The dynamic adjustment strategy feeds feedback data into the customized model in real time, calculates the deviation between the predicted and actual values, and if the deviation is greater than 5%, the scaling factor of the customized model is revised and the model is updated.
[0045] This invention presents a dynamic analysis and control method for the offshore LNG carrier-to-ship bunkering process. It constructs a fully intelligent management and control system encompassing data preprocessing, dynamic modeling, operational condition correction, dynamic regulation, and closed-loop optimization, achieving precise, safe, and efficient control of the bunkering process. Step S1, through real-time data acquisition and standardized preprocessing, solves the problems of non-standard data acquisition, outlier interference, and inconsistencies between multi-source data in traditional operations. It simultaneously collects basic bunkering parameters of the bunkering vessel and information of the receiving vessel, covering core variables throughout the bunkering process and avoiding data omissions. The data processing module performs preprocessing such as outlier removal and data smoothing, generating a standardized dataset. This eliminates the interference of data fluctuations and noise on subsequent modeling and regulation, significantly improving the robustness of the entire bunkering system and providing reliable data support for subsequent accurate modeling and dynamic regulation. Steps S2 and S3, through dynamic model construction and operational condition matching and correction, solve the problems of traditional methods relying on crew experience and having poor adaptability. A general dynamic numerical analysis model characterizing the LNG refueling process is constructed. Based on the physical principles of gas-liquid phase change and the law of heat transfer conservation, core processes such as BOG generation and cargo hold temperature and pressure changes during refueling are quantified. This achieves calculable and predictable refueling status at the principle level, completely eliminating reliance on human experience. By matching and correcting the model with standardized datasets and historical operational condition data, a customized model adapted to the current refueling scenario is generated. This model can adapt to different refueling vessel hold types, refueling volumes, initial cargo hold states, and other varying operational conditions. The model's prediction error is controllable, significantly improving model accuracy and scenario adaptability. Steps S4 and S5, through dynamic control strategies and closed-loop optimization, solve the problems of balancing safety and efficiency and high cargo damage rates in traditional operations.
[0046] This invention uses a customized model to calculate BOG generation and cargo hold temperature and pressure trends in real time. Combined with a dynamic control strategy centered on BOG handling capacity, it generates control commands to stably control BOG generation and maintain tank pressure within the safe threshold of the receiving vessel's design pressure. This proactively avoids safety risks such as tank pressure overpressure and BOG exceeding limits, significantly reducing the probability of safety accidents such as overpressure and leakage. Through a closed-loop judgment process, the refueling rate is continuously verified. If the optimal rate is not reached, it is cyclically adjusted until the optimal refueling rate is locked, maximizing refueling efficiency under the premise of safety. This avoids the extreme situations in traditional manual operations where excessive speed reduction is used to ensure safety, or safety is ignored in order to improve efficiency. The refueling operation time can be significantly shortened, and the refueling efficiency can be greatly improved. Precise control of BOG generation reduces cargo loss caused by direct BOG emissions, reducing the LNG cargo loss rate to an extremely low level and significantly improving the economics of refueling operations.
[0047] This invention establishes a fully automated and intelligent control system that eliminates the need to rely on the personal experience of crew members, reducing the risk of human error. At the same time, it standardizes the LNG ship-to-ship bunkering operation process, allowing bunkering operations by different crew members and different vessels to follow a unified and precise standard, improving the standardization and consistency of operations. It is adaptable to various LNG ship-to-ship bunkering scenarios and provides technical support for the large-scale and standardized promotion of LNG bunkering operations.
[0048] 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 of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims
1. A method for dynamic analysis and control of LNG carrier-to-ship bunkering process at sea, characterized in that, Includes the following steps: S1, the data acquisition module collects the basic refueling parameters of the refueling vessel in real time, the data acquisition module obtains the information of the receiving vessel, and the data processing module performs data preprocessing on the basic refueling parameters and the information of the receiving vessel to obtain a standardized dataset; S2, Construct a dynamic numerical analysis model to characterize the LNG refueling process; S3, Match the dynamic numerical analysis model, the standardized dataset, and the historical working condition data to correct the dynamic numerical analysis model into a customized model; S4, set a dynamic control strategy, combine the customized model with the dynamic control strategy to generate a refueling rate control command, and the refueling vessel controls the LNG refueling operation according to the refueling rate control command; S5, determine whether the injection rate after adjustment in step S4 is the optimal injection rate; If the result is negative, return to step S4; If the judgment result is yes, the bunkering vessel will continue bunkering until the LNG bunkering operation is completed.
2. The method for dynamic analysis and control of LNG carrier-to-ship bunkering process at sea according to claim 1, characterized in that, The basic refueling parameters include cargo hold temperature, cargo hold pressure, cargo hold density, cargo hold liquid level, LNG refueling rate, LNG flow rate, and pump speed.
3. The method for dynamic analysis and control of LNG carrier-to-ship bunkering process at sea according to claim 2, characterized in that, The data preprocessing in step S1 includes the following steps: When the pump power of the refueling vessel reaches its maximum, it is the full-speed stage; as the pump power gradually increases, it is the acceleration stage. The data processing module acquires basic refueling parameters and recipient vessel information. After the refueling vessel begins refueling LNG at a set time, the data processing module checks the basic refueling parameters according to a threshold judgment formula. The threshold judgment formula is as follows: in, Threshold for LNG flow rate determination For LNG flow rate, This represents the average flow rate monitored for LNG. The cargo hold temperature threshold. For cargo hold temperature, This represents the average temperature monitored in the cargo hold. This represents the temperature standard deviation. This refers to the pump speed; The data processing module performs a data removal operation to remove basic annotation parameters that do not conform to the threshold judgment formula.
4. The method for dynamic analysis and control of LNG carrier-to-ship bunkering process at sea according to claim 3, characterized in that, When in the full-speed phase, the data processing module performs a data removal operation every 1 second; when in the acceleration phase, the data processing module performs a data removal operation every 10 seconds.
5. The method for dynamic analysis and control of LNG carrier-to-ship bunkering process at sea according to claim 1, characterized in that, Step S2 includes: The dynamic numerical analysis model is as follows: in, To generate quality for BOG, Let be the heat transfer coefficient of the liquid phase. Where is the heat transfer coefficient of the gas phase. Gas-liquid interface area For the enthalpy of the liquid phase, Enthalpy in the gas phase, The first scaling factor, The second scaling factor, For interface temperature, The temperature of LNG, For the temperature of BOG, For the heat transfer from the interface to the liquid phase, This refers to the heat transfer from the interface to the gas phase.
6. The method for dynamic analysis and control of LNG carrier-to-ship bunkering process at sea according to claim 5, characterized in that, The historical operating data includes the cargo hold type of the receiving vessel, the amount of fuel added, the temperature inside the cargo hold of the receiving vessel, and the initial tank pressure of the receiving vessel.
7. The method for dynamic analysis and control of LNG carrier-to-ship bunkering process at sea according to claim 6, characterized in that, When the cargo hold type of the receiving vessel is Type C, the interface is in thermal equilibrium, the temperature variation range inside the cargo hold of the receiving vessel is -150℃ to -120℃, and the tank pressure of the receiving vessel is 0.1MPa to 0.5MPa, The customized model is as follows: in, This is the saturated steam temperature.
8. The method for dynamic analysis and control of LNG carrier-to-ship bunkering process according to claim 7, characterized in that, The dynamic control strategy includes the following steps: The customized model analyzes the current BOG generation, cargo hold temperature, and cargo hold pressure, and predicts the changing trends of BOG generation, cargo hold temperature, and cargo hold pressure within a set time period. Based on the changing trends, it is determined whether the BOG generation or cargo hold pressure exceeds the standard. If the result is negative, maintain the current betting rate; If the judgment result is yes, an alarm command is triggered and a control command is output. After control, the BOG generation or cargo hold pressure is re-judged to see if it exceeds the standard. The control instructions are as follows: in, For comprehensive correction factors, The maximum allowable processing quality for BOG is preset. The current LNG refueling flow rate is collected by the data acquisition module. The LNG refueling flow rate is to be adjusted.
9. The method for dynamic analysis and control of LNG carrier-to-ship bunkering process at sea according to claim 8, characterized in that, The formula for calculating the amount of BOG generated during the refueling process, based on the customized model, is as follows: in, For cargo hold heat exchange area, Due to the temperature difference between the inside and outside of the cargo hold, The heat transfer coefficient is... As latent heat of vaporization, To extend the duration, For BOG generation quantity; The formula for calculating cargo hold pressure during the refueling process, based on a customized model, is as follows: in, For cargo hold pressure, Initial chamber pressure, This refers to BOG emissions. For cargo hold gas phase space volume, For LNG molar mass, is the gas constant.
10. The method for dynamic analysis and control of LNG carrier-to-ship bunkering process according to claim 1, characterized in that, The data acquisition module includes a sensor network, which is located at the outlet of the bunkering vessel's output pipeline, the LNG transmission pipeline, the LNG cargo tank of the bunkering vessel, and the liquid dome area.