Methods and systems for assisting in the management of liquefied gas carriers that use evaporated gas for propulsion.

Abstract

The present invention relates to a method for assisting in the management of a ship, the ship including a vapor phase treatment system (10) and at least one tank (3) for containing liquefied gas, the vapor phase treatment system being capable of sending evaporated gas leaving the tank to the ship's propulsion engine (40) or to a gas combustion unit (30) installed on the ship, and being capable of removing a portion of the liquid phase contained in the tank and evaporating that portion to send it to the propulsion engine (40). The method includes: generating (305) at least one tank management scheme that limits the pressure variation of the vapor phase contained in the tank along the ship's trajectory; calculating (404) a cost function that depends at least on the total amount of evaporated gas generated in the tank along the trajectory; and displaying the tank management scheme to a user based on the calculated cost function.

Description

Technical Field

[0001] This invention relates to the field of ships used for transporting liquefied gases, where the consumption of evaporated gas is used for propulsion. More specifically, the invention proposes methods and systems for assisting in the management of such ships. Background Technology

[0002] Sealed and thermally insulated tanks are commonly used for transporting liquefied natural gas (LNG) at approximately -162°C at atmospheric pressure. These tanks can be used for transporting liquefied gases. Various liquefied gases can also be considered, particularly methane, ethane, propane, butane, ammonia, dihydrogen, or ethylene.

[0003] Ship tanks can be single- or double-layered sealed membrane tanks that allow transport at atmospheric pressure. The sealing membrane is typically made of thin sheets of stainless steel or Invar. The membrane is usually in direct contact with the liquefied gas.

[0004] Because liquefied gases are transported at temperatures significantly below ambient temperature, when a ship carrying the gas, which includes one or more such tanks, naturally tends to heat up slowly, even though the tanks are thermally insulated. The gas produced by this heating is commonly referred to as boil-off gas (BOG). For example, when transporting LNG at approximately -162°C at atmospheric pressure, approximately 0.05% to 0.15% of the volume of LNG initially contained in the tanks can be observed to be produced daily.

[0005] To utilize BOG (Bound Gauge), it is known to use all or some of the BOG for propulsion by sending it to the ship's propulsion engines capable of consuming BOG. ​​Ships are also typically equipped with a gas combustion unit (GCU) to burn any excess BOG that cannot be consumed by the ship's propulsion engines.

[0006] The BOG burned in the GCU along the ship's path represents a loss, because this burned BOG is not delivered to the ship and is consumed without any benefit. Therefore, it is ideal to minimize the amount of this burned BOG along a given path of the ship. Furthermore, even if the BOG sent to the ship's propulsion engines is utilized, this also represents a reduction in the amount delivered to the ship's destination.

[0007] In addition, there is a need for a solution to assist in the management of the ship, which allows for the limitation of the total amount of BOG generated in the tank, in order to limit the amount of BOG burned in the GCU and the amount of BOG sent to the ship's propulsion engines. Summary of the Invention

[0008] One idea behind this invention is to use weather forecasts along a predicted path of the ship and a model of the ship's operation to estimate at least the total amount of vaporized gas generated in the ship's tanks. Another idea behind this invention is to perform this estimation for at least one, and preferably more, tank management schemes that define the evolution of pressure of the gaseous phase contained in the tanks along the predicted path. Yet another idea behind this invention is to display to the user at least one of the tank management schemes according to a cost function that depends at least on the total amount of vaporized gas generated in the tanks along the predicted path.

[0009] According to one embodiment, the present invention provides a computer-implemented method for assisting in the management of a ship used for transporting liquefied gases. The ship includes a vapor phase treatment system and at least one tank configured to contain the liquefied gases. The vapor phase treatment system is capable of sending evaporated gas exiting the tank to the ship's propulsion engine or to a gas combustion unit on board. The vapor phase treatment system is also capable of removing a portion of the liquid phase contained in the tank and evaporating that portion to send it to the propulsion engine. The method includes:

[0010] - Provide the initial state of the tank, which includes the initial pressure of the gas phase contained in the tank and the initial temperature of the liquid phase contained in the tank;

[0011] - Provides predicted routes for ships;

[0012] - Determine at least one environmental parameter of the ship along the predicted path based on weather forecasts;

[0013] - Determine the speed curve of the ship along the predicted path;

[0014] - Generate a tank management scheme that defines the evolution of the pressure of the gaseous phase contained in the tank along the predicted path, and, based on the tank management scheme generated in this way:

[0015] a) Estimate the curve of the power required to be output from the propulsion engine along the predicted path based on the ship's speed curve and the at least one environmental parameter.

[0016] b) Estimate the curve of the amount of evaporated gas generated in the tank along the predicted path;

[0017] c) Based on the required power curve and the curve of the amount of evaporated gas produced, estimate the curves of the amount of gas to be removed from the tank and the amount of evaporated gas to be burned in the gas combustion unit; and

[0018] d) Calculate a cost function that depends at least on the total amount of evaporated gas generated in the tank along the predicted path; and

[0019] - Display tank management solutions to users based on the calculated cost function.

[0020] Because of these characteristics, tank management solutions can be evaluated by calculating cost functions and displayed to users to provide decision support.

[0021] According to the implementation method, this method may include one or more of the following features.

[0022] According to one implementation, multiple tank management schemes are generated, and steps a) to d) are performed for each tank management scheme, and at least one of the tank management schemes is displayed to the user based on a calculated cost function of the tank management scheme.

[0023] Therefore, a large number of tank management schemes can be evaluated by calculating the cost function, and users can be shown one or more tank management schemes that optimally minimize the cost function to provide decision support.

[0024] According to one implementation, before displaying at least one of the tank management schemes to the user, the tank management schemes are iteratively regenerated multiple times using a first evolutionary algorithm that uses a cost function as a first objective function.

[0025] The term "evolutionary algorithm" is understood to refer to a method, typically implemented by a computer, in which a set of solutions is generated, each solution is evaluated against an objective function, some solutions that optimally minimize the objective function are selected, a new set of solutions is generated from these selected solutions, and these steps are repeated as long as the stopping criterion is not validated. Within the scope of this invention, each tank management scheme is a solution, and the cost function serves as the objective function of the evolutionary algorithm.

[0026] Various types of evolutionary algorithms are known. In one implementation, the evolutionary algorithm is the genetic algorithm. Evolutionary algorithms are particularly well-suited for optimization problems.

[0027] As mentioned above, evolutionary algorithms must use stopping criteria to determine when to stop searching for more optimal solutions. According to one implementation, the stopping criterion is a computation time criterion that ensures the method displays one or more optimized tank management schemes to the user at the end of a given computation time.

[0028] According to one implementation, the ship's predicted path is divided into consecutive time intervals, and one or each tank management scheme defines the average pressure of the gaseous substance contained in the tank during each of the consecutive time intervals.

[0029] According to one embodiment, the at least one tank management scheme displayed to the user is shown together with the minimum pressure of the gaseous substance contained in the tank and / or the maximum pressure of the gaseous substance contained in the tank during each time period in the continuous time interval.

[0030] According to one embodiment, the predicted path includes a path step defined by two waypoints and a heading to be followed between the two waypoints, and in step a), the curve of power required to be output from the propulsion engine along the predicted path is estimated based on the heading to be followed, the ship's speed curve, and the at least one environmental parameter.

[0031] In this way, the evaluation of the tank management scheme also takes into account the impact of the ship's course on the power required to be output from the propulsion engine. In fact, for example, this power can be higher or lower depending on the angle between the ship's course and the direction of ocean currents, wind, and waves.

[0032] According to one embodiment, the ship's speed curve is determined using a second evolutionary algorithm with a second objective function based on the predicted path and the at least one environmental parameter. The second objective function depends on the total amount of vaporized gas generated in the tank along the predicted path and the difference between the required time to reach the destination and the estimated time to reach the destination.

[0033] In this way, the required time for the ship to arrive is estimated by taking into account the power output from the propulsion engine, wherein the time can be specified by the user as an input parameter.

[0034] Terminals used for systematically unloading liquefied gases require tanks with a given unloading pressure and / or unloading temperature. According to one embodiment, providing a predicted route for a vessel includes providing the required unloading pressure and / or unloading temperature at the vessel's destination unloading terminal, and in step d), the cost function further depends on the difference between the pressure of the gaseous phase contained in the tank at the end of the predicted route and the unloading pressure, and / or the difference between the temperature of the liquid phase contained in the tank at the end of the predicted route and the unloading temperature.

[0035] In this way, a tank management scheme is considered even more acceptable when it allows the tank to reach the unloading terminal at the destination point with unloading pressure and / or unloading temperature close to the unloading pressure and / or unloading temperature required by the unloading terminal.

[0036] According to one implementation, in step a), a first statistical model is used to estimate the required power curve. The first statistical model is capable of estimating the power required to be output from the propulsion engine based on the ship's speed setpoint and the at least one environmental parameter, and the first statistical model is trained using a supervised machine learning method.

[0037] The term "supervised machine learning method" or "supervised learning" is understood to refer to machine learning methods that involve learning a prediction function based on annotated examples. In other words, supervised machine learning methods allow for the construction of models capable of making predictions from multiple examples where the responses to be predicted are known. Supervised machine learning methods are typically implemented by computers; therefore, the training of the initial statistical model is usually done by a computer.

[0038] Of course, it is understandable that in the sentence or feature stating “the statistical model is trained using a supervised machine learning method on the test dataset”, the “test dataset” may also include data derived from or composed of data derived from “real” activities, namely data obtained or recorded on ships that are circulated as transporters and users of liquefied gases.

[0039] According to one embodiment, step b) above, which involves estimating the curve of the amount of evaporated gas generated in the tank along a predicted path, includes: estimating a first amount of evaporated gas generated in the tank based on the current pressure of the gas phase contained in the tank and the current temperature of the liquid phase contained in the tank; and estimating a second amount of evaporated gas generated in the tank based on the current pressure of the gas phase contained in the tank and the at least one environmental parameter.

[0040] Therefore, the estimation of the amount of vaporized gas generated in the tank takes into account both the generation of vaporized gas related to the coexistence of liquid and gaseous substances in the tank and the vaporized gas generated due to the agitation of the liquid substances caused by the ship's motion.

[0041] According to one implementation, a second amount of evaporated gas generated in the tank is estimated using a second statistical model, which is trained using a supervised machine learning method.

[0042] According to one embodiment, the initial state includes the initial composition of the liquefied gas contained in the tank, and step b) which involves estimating the amount of vaporized gas generated in the tank along the predicted path also includes estimating the composition of the vaporized gas generated in the tank.

[0043] Liquefied gas cargoes are almost never chemically pure: they are typically mixtures of multiple liquefied gases, with one gas being predominant by weight and one or more others present as impurities. However, the initial composition of the cargo is usually indicated by the liquefied gas supplier when the cargo is loaded into the tank. By indicating the initial composition of the liquefied gases and estimating the composition of the vaporized gases produced in the tank, the fact that the composition of the vaporized gases will evolve along a predicted path due to the various evaporation characteristics of the gases forming the liquefied gas cargo is taken into account.

[0044] According to one implementation, the environmental parameters include at least one of ocean current direction, ocean current speed, wind speed, wind direction, mean wave height, wave direction, and wave period.

[0045] According to one embodiment, the vapor phase treatment system further includes a reliquefaction facility capable of reliquefying the evaporated gas leaving the tank and returning the reliquefied evaporated gas back to the tank, and step c) above includes: estimating, based on the curves of the required power curve and the amount of evaporated gas produced, the curves of the amount of gas to be taken out of the tank, the amount of evaporated gas to be burned in the gas combustion unit, and the amount of evaporated gas to be sent to the reliquefaction facility.

[0046] According to one embodiment, the vapor phase processing system further includes a subcooler capable of subcooling a portion of the liquid phase contained in the tank and returning such subcooled portion to the tank, wherein step c) comprises: estimating, based on the curve of the desired power curve and the curve of the amount of vaporized gas produced, the curve of the amount of vaporized gas to be removed from the tank, the curve of the amount of vaporized gas to be burned in the gas combustion unit, and the curve of the amount of liquid phase to be removed from the tank and sent to the subcooler.

[0047] According to one implementation, the steps of the method are repeated at fixed intervals and / or when a new weather forecast is received.

[0048] Therefore, the method periodically re-evaluates the tank management plan as the ship travels along its predicted path and when new weather forecasts become available. Additionally or alternatively, the steps of the method can be repeated upon user command.

[0049] According to one embodiment, the present invention also provides a system for assisting in the management of a ship used for transporting liquefied gases. The ship includes a vapor phase treatment system and at least one tank configured to contain the liquefied gases. The vapor phase treatment system is capable of sending evaporated gas exiting the tank to the ship's propulsion engine or to a gas combustion unit on board. The vapor phase treatment system is also capable of removing a portion of the liquid phase contained in the tank and evaporating that portion to send it to the propulsion engine. The management assistance system includes:

[0050] - An acquisition unit configured to acquire the initial state of the tank and the predicted path of the ship, the initial state of the tank including the initial pressure of the gas phase contained in the tank and the initial temperature of the liquid phase contained in the tank;

[0051] - A first communication unit configured to receive weather forecasts from a weather forecast provider; and

[0052] - A calculation unit configured to: determine the velocity profile of the ship along the predicted path; generate a tank management scheme that defines the pressure evolution of the gaseous phase contained in the tank along the predicted path; and based on the tank management scheme thus generated, the calculation unit is configured to:

[0053] a) Estimate the curve of the power required to be output from the propulsion engine along the predicted path based on the ship's speed curve and the at least one environmental parameter.

[0054] b) Estimate the curve of the amount of evaporated gas generated in the tank along the predicted path;

[0055] c) Based on the required power curve and the curve of the amount of vaporized gas produced, estimate the curve of the amount of gas to be taken out of the tank and the curve of the amount of vaporized gas to be burned in the gas combustion unit.

[0056] d) Calculate a cost function that depends at least on the total amount of evaporated gas generated in the tank along the predicted path; and includes...

[0057] - Display unit, which is configured to display the tank management scheme to the user based on a calculated cost function.

[0058] This system offers the same advantages as the methods described above.

[0059] According to one embodiment, the predicted path includes a path step defined by two waypoints and a heading to be followed between the two waypoints, and wherein the computing unit is configured to: a) estimate a curve of the power required to be output from the propulsion engine along the predicted path based on the heading to be followed, the ship's speed curve, and the at least one environmental parameter.

[0060] According to one embodiment, the acquisition unit and display unit are located on a ship, the first communication unit and computing unit are located in a ground station, and the system further includes a second communication unit configured to connect the acquisition unit and the computing unit. Attached Figure Description

[0061] The invention will be better understood in the following description of several specific embodiments of the invention, provided only by way of non-limiting illustration, with reference to the accompanying drawings, and other objects, details, features, and advantages of the invention will become more apparent.

[0062] [ Figure 1 ] Figure 1 This is a schematic diagram of a ship used for transporting liquefied gases.

[0063] [ Figure 2 ] Figure 2 It is shown illustratively. Figure 1 A block diagram of the ship's tanks and vapor phase treatment system, the ship's propulsion engine, and the ship's gas combustion unit.

[0064] [ Figure 3 ] Figure 3 It is used for Figure 1 A block diagram of a system that assists in the management of ships.

[0065] [ Figure 4 ] Figure 4 It is based on Figure 3 Alternative implementation methods for... Figure 1 A block diagram of a system that assists in the management of ships.

[0066] [ Figure 5A ] Figure 5A It shows the method for... Figure 1 A block diagram of the first steps of a method to assist in the management of ships, a method that can be... Figure 3 This is achieved through a system designed to assist in management.

[0067] [ Figure 5B ] Figure 5B It shows Figure 5A A flowchart of the subsequent steps of the method.

[0068] [ Figure 6 ] Figure 6 An example of a ship's predicted path is shown as an illustration.

[0069] [ Figure 7 ] Figure 7 It is shown by way of example in Figure 3 A diagram illustrating an example of a tank management scheme generated in a system used to assist in management.

[0070] [ Figure 8 ] Figure 8 It is shown in an illustrative manner. Figure 3 A block diagram of a model used to assist in management and to evaluate a given tank management scheme.

[0071] [ Figure 9 ] Figure 9 The maximum and minimum pressure values ​​in the tank are illustrated using examples. Figure 7 A diagram illustrating an example of a similar tank management scheme.

[0072] [ Figure 10 ] Figure 10 The example shows the subdivision of time intervals. Figure 7 The diagram illustrates a similar tank management scheme.

[0073] [ Figure 11 ] Figure 11 The following is an example illustrating some implementation methods. Figure 3 A diagram illustrating a speed management scheme generated in a system used to assist in management.

[0074] [ Figure 12 ] Figure 12 It is shown in an illustrative manner. Figure 3 A block diagram of a model used by a system to assist in management in order to evaluate a given speed management scheme.

[0075] [ Figure 13 ] Figure 13 This illustrates an alternative implementation of the vapor phase processing system. Figure 2 A block diagram similar to the one shown.

[0076] [ Figure 14 ] Figure 14 This illustrates another alternative implementation of the vapor phase processing system. Figure 2 A block diagram similar to the one shown.

[0077] [ Figure 15 ] Figure 15 This illustrates yet another alternative implementation of the vapor phase processing system. Figure 2 A block diagram similar to the one shown. Detailed Implementation

[0078] The embodiments described below are for a ship comprising a double hull forming a support structure in which a sealed and thermally insulated tank is arranged. In such a support structure, the tank has, for example, a polyhedral geometry, such as a prism shape.

[0079] Such sealed and thermally insulated tanks are designed, for example, for the transport of liquefied gases. The liquefied gases are stored and transported at low temperatures in these tanks, which require thermally insulated tank walls to maintain the liquefied gases at that temperature.

[0080] This sealed and thermally insulated container also includes an insulating barrier anchored to the vessel's double hull and supporting at least one sealing membrane. For example, such a container can be configured according to the applicant's Mark... or The technology or similar technology sold under the brand is used for production.

[0081] Figure 1 The vessel 1 is shown, which includes four sealed and thermally insulated tanks 2. The four tanks 2 may have the same or different filling states.

[0082] Figure 2 It is shown schematically. Figure 1 A diagram showing the four tanks, 3, 4, 5, and 6. (See diagram below.) Figure 2 As shown, the ship 1 is also equipped with a vapor phase treatment system 10, an engine 40, and a gas combustion unit (GCU) 30.

[0083] Engine 40 provides the power required to propel the vessel 1; alternatively, engine 40 can provide both the power required to propel the vessel 1 and the power required to electrically drive other equipment on the vessel 1 (often referred to as "hotel load"). Engine 40 is capable of consuming gas in the vapor phase, which may originate from evaporating gas (BOG) exiting from tanks 3, 4, 5, and 6, or from heater 60, which is used to evaporate liquefied gas drawn from the tanks. Such an engine 40 is known and therefore will not be described in detail herein.

[0084] Still in a manner known per se, GCU 30 is able to combust any excess BOG that cannot be consumed by engine 40. This GCU 30 is known and therefore will not be described in detail herein.

[0085] The vapor phase material handling system 10 is capable of sending BOG (Body Gas) exiting from each of tanks 3, 4, 5, and 6 to engine 40 or GCU 30, as appropriate. To this end, the vapor phase material handling system 10 includes vapor phase material handling subsystems 13, 14, 15, and 16 for each of tanks 3, 4, 5, and 6, and a BOG distribution unit 20 communicating with these subsystems 13, 14, 15, and 16 to allow BOG to be sent to engine 40 or GCU 30, as appropriate. Such vapor phase material handling systems and subsystems are known and therefore will not be described in detail herein.

[0086] Still by means known per se, each subsystem 13, 14, 15, 16 is also able to remove a portion of the liquid phase contained in the respective tanks 3, 4, 5, 6 when needed and send that portion to the heater 60 for evaporation.

[0087] Although a vessel 1 with four tanks has been shown, it is clear that the vessel 1 can be equipped with a different number of tanks, in particular a single tank or even any number of tanks.

[0088] In the following description, and for convenience, management assistance method 300 will be referred to tank 3, and it will be understood that management assistance method 300 can be implemented simultaneously for multiple tanks in tanks 3, 4, 5, and 6, or for each tank.

[0089] Figure 3 An example of a management support system 100 on ship 1 is shown. The management support system 100 includes a central unit 110 connected to a communication interface 130, a human-machine interface 140, and a database 150.

[0090] The communication interface 130 allows the central unit 110 to communicate with remote devices, such as to obtain weather data, ship position data, etc.

[0091] The human-machine interface 140 includes a display device 41. The display device 41 allows the user to see one or more tank management schemes 50, as will be described below.

[0092] The human-machine interface 140 also includes an acquisition device 42 that allows the user to manually provide quantities to the central unit 110, as will be described below.

[0093] The management support system 100 also includes a database 150. This database 150 can be used to store models, particularly models 61, 62, and 63, as will be described below.

[0094] Figure 4An example of a management assistance system 200 is shown, which is located on land and communicates with a ship 1. The ship 1 includes a central unit 210, a human-machine interface 130, and a communication interface 230. The central unit 110, communication interface 130, and database 150 are, in themselves, located in a ground station 1000. The operation of the management assistance system 200 is similar to that of the management assistance system 100, except that the results of calculations performed by the land-based central unit 110 are transmitted to the human-machine interface 140 on the ship 1 via communication interfaces 130 and 230. For example, communication interfaces 130 and 230 can use terrestrial or satellite radio frequency data transmission.

[0095] Reference Figures 5A to 9 The method 300 for assisting in the management of ship 1 will now be described, which can be implemented using management assistance system 100 or 200.

[0096] Method 300 includes a first step 301, which relates to providing an initial state of tank 3. This initial state includes the initial pressure of the gaseous phase 3G contained in tank 3 and the initial temperature of the liquid phase 3L contained in tank 3. This information is input, for example, by a user using an acquisition device 42.

[0097] Preferably, the initial state also includes the initial composition of the liquefied gas contained in tank 3. This initial composition is typically indicated by the liquefied gas supplier when the liquefied gas is loaded into tank 3. This initial composition can be input, for example, by a user using the dispensing device 42.

[0098] Method 300 also includes step 302, which involves providing a predicted path 80 for vessel 1. Figure 6 An example of a predicted path 80 is shown as an illustration. As shown in the figure, the predicted path 80 is defined by a starting point 81, a destination point 82, and multiple waypoints 83 that ship 1 will pass through. Points 81, 82, and 83 are input by a user, for example, using acquisition device 42. Points 81, 82, and 83 are defined by the geographic coordinates of these points in a manner known per se.

[0099] Each pair of consecutive waypoints 83 together defines path step 84. The predicted path 80 can also be defined by the heading that ship 1 will follow along path step 84.

[0100] Method 300 further includes step 303, which involves determining at least one environmental parameter of the ship 1 along the predicted path 80 based on a weather forecast. Specifically, the central unit 110 uses communication interface 130 to obtain a weather forecast provided by a weather forecast provider, and the central unit 110 determines at least one environmental parameter based on the weather forecast and the predicted path 80. The environmental parameter includes at least one of ocean current direction, ocean current speed, wind speed, wind direction, mean wave height, wave direction, and wave period, or preferably, the environmental parameter includes multiple or even all of these parameters. It should be noted that some of the environmental parameters can be directly provided by the weather forecast provider, while other environmental parameters can be estimated by the central unit 110 by applying a model to the weather forecast.

[0101] Method 300 further includes step 304, which involves determining the velocity profile of the ship 1 along the predicted path 80. In a simplified embodiment, the velocity profile can simply be provided as input data, for example, by a user using acquisition device 41.

[0102] Method 300 also includes step 305, which involves generating multiple tank management schemes 50.

[0103] Figure 7 This diagram illustrates an example of a tank management scheme 50. As shown in the diagram, the given tank management scheme 50 defines the pressure P of the gaseous phase 3G contained in the tank 3 during the predicted path according to the evolution of time t. In a preferred embodiment, when the predicted path 80 is divided into consecutive time intervals d1, d2, d3, d4, d5, d6, etc., the tank management scheme 50 defines average values ​​P1, P2, P3, P4, P5, P6, etc., of the pressure P over the time intervals d1, d2, d3, d4, d5, d6, etc. Specifically, each of the time intervals d1, d2, d3, d4, d5, d6 can be equal to one day.

[0104] The tank management scheme 50 can be generated randomly, for example, in step 305.

[0105] After step 305, method 300 proceeds to evaluate tank management scheme 50. Figure 5A and Figure 5B (Figure A in the figure).

[0106] According to the preferred embodiment, which will be described in further detail below, method 300 uses a cost function as the objective function to implement the evolutionary algorithm, the cost function depending at least on the total amount of BOG generated in tank 3 along the prediction path 80. In the context of this evolutionary algorithm, tank management scheme 50 is iteratively regenerated multiple times in a manner known per se, wherein each newly generated tank management scheme 50 is generated based on a certain number of previously generated tank management schemes 50 that optimally minimize the cost function.

[0107] Figure 8 Block diagrams of models 61, 62, and 63 are shown for the purpose of understanding the following description, which are implemented for evaluating a cost function. These models 61, 62, and 63 can be stored in the database 150 of the management support system 100.

[0108] The first model 61, referred to below as "engine model 61," is capable of estimating the power required to be output from engine 40. More specifically, the first model 61 is capable of estimating the output shaft 40A of engine 40 (see [link to model 61]). Figure 2 The required mechanical power is estimated. This mechanical power may be solely the power required to propel the vessel 1. Alternatively, the mechanical power may be the sum of the power required to propel the vessel 1 and the power required to electrically drive other equipment on the vessel 1 (often referred to as "hotel loads"). In either case, engine model 61 is able to estimate the power required to be output from engine 40 based on the vessel 1's speed setpoint, at least one environmental parameter, and optionally, the vessel 1's heading.

[0109] According to a specific implementation, engine model 61 is a statistical model trained using a supervised machine learning method. This statistical model is trained based on a test dataset, which can be obtained from navigation tests involving sailing the ship and measuring the following:

[0110] - On the one hand, the ship's speed and at least one environmental parameter, the at least one environmental parameter being selected from wave height, wave period, the angle between wave direction and ship's course, wind speed, and the angle between wind direction and ship's course;

[0111] - On the other hand, the power required to maintain the speed of the boat comes from the engine.

[0112] The second model 62, referred to below as "tank model 62," is capable of estimating the amount of BOG generated in tank 3. Tank model 62 estimates the amount of BOG generated in the tank in the following manner:

[0113] - Estimate the first amount of BOG produced in tank 3 based on the current pressure P of the gas phase 3G contained in tank 3 and the current temperature T of the liquid phase 3L contained in tank 3; and

[0114] - Estimate a second amount of BOG generated in tank 3 based on the current pressure P of gas phase 3G and at least one environmental parameter, and then add the two amounts of BOG together.

[0115] The initial quantity of BOG reflects the generation of BOG associated with the coexistence of liquid phase 3L and gas phase 3G. The initial quantity of BOG can be estimated using known thermodynamic models, such as the Hertz-Knudsen model.

[0116] The second amount of BOG reflects the additional BOG generated in tank 3 due to the agitation of liquid phase 3L caused by the motion experienced by ship 1.

[0117] According to a specific implementation, the second quantity of BOG is estimated by a statistical model trained using a supervised machine learning method. This statistical model is trained on a test dataset, which can be obtained from navigation tests involving sailing the ship and measuring the following:

[0118] - On the one hand, the pressure of the gaseous phase contained in the tank and the temperature of the liquid phase contained in the tank; and

[0119] - On the other hand, there is a difference between the amount of BOG actually generated in the ship's tanks and the estimated amount of BOG estimated by thermodynamic models.

[0120] Furthermore, as described above, if the initial composition of the liquefied gas contained in tank 3 is provided in step 301, tank model 62 can also estimate the composition of the BOG generated in tank 3. In this case, the model that estimates the first amount of BOG and the model that estimates the second amount of BOG each estimate the composition of the BOG generated in tank 3.

[0121] The third model 63, referred to below as the "flow model," estimates the amount of gas removed from tank 3 by the vapor phase treatment subsystem 13 and the amount of BOG to be burned in GCU 30. More specifically, based on the power estimated by model 61, flow model 63 estimates the amount of BOG to be supplied to engine 40, and compares this amount of BOG to be supplied to engine 40 with the amount of BOG generated in tank 3 estimated by tank model 62. If the amount of BOG to be supplied to engine 40 is less than the amount of BOG generated in tank 3, flow model 63 determines that the difference between these two amounts will be burned in GCU 30. Conversely, if the amount of BOG to be supplied to engine 40 is greater than the amount of BOG generated in tank 3, flow model 63 determines that the difference between these two amounts will be removed from tank 3 by vapor phase treatment subsystem 13.

[0122] Models 61, 62, and 63 are implemented iteratively over time intervals that are very small relative to the durations of intervals d1, d2, etc., to estimate the pressure P of the gas phase 3G, the temperature T of the liquid phase 3L, the amount of BOG generated by tank 3 (and, where appropriate, the composition of the BOG generated by tank 3), and the amount of BOG to be supplied to engine 40 and GCU 30 in each iteration.

[0123] Referring further to 5B, the steps for successfully evaluating the cost function will now be described. When N is the number of tank management schemes 50 generated in step 305, method 300 evaluates each of the tank management schemes 50 N times, S1, S2, ..., SN. Although Figure 5B The diagram shows the evaluations S1, S2, ..., SN as performed sequentially, but optionally, these evaluations can be performed simultaneously. Each evaluation S1, S2, ..., SN includes steps 401, 402, 403, 404 as described below.

[0124] In step 401, based on the speed curve of ship 1 determined in step 304 and at least one environmental parameter determined in step 303, engine model 61 is used to estimate the curve of the power required to be output from engine 40 along the predicted path 80.

[0125] In step 402, the amount of BOG generated in tank 3 along the predicted path 80 is estimated using tank model 62.

[0126] In step 403, based on the curve of the power required to be output from engine 40 estimated in step 401 and the curve of the amount of BOG generated in tank 3 estimated in step 402, flow model 63 is used to estimate the curve of the amount of gas to be taken out of tank 3 and the curve of the amount of BOG to be burned in GCU 30.

[0127] Following steps 401 to 403, the cost function is calculated in step 404. More specifically, the cost function is a real number calculated based on the estimates made in steps 401 to 403 and quantifies the acceptability of the relevant tank management scheme 50.

[0128] In a simplified implementation, the cost function depends only on the total amount of BOG generated in tank 3 along the prediction path 80. However, preferably, the cost function also depends on other quantities or criteria.

[0129] In a preferred embodiment, the cost function also depends on:

[0130] - The difference between the pressure P of the gaseous phase contained in tank 3 at the end of predicted path 80 and the unloading pressure required at the unloading terminal at destination 82; and / or

[0131] - The difference between the temperature T of the liquid phase contained in tank 3 at the end of the predicted path 80 and the required unloading temperature at the unloading terminal located at destination point 82; and

[0132] - Both of these differences are preferred. In this way, tank management scheme 50 is considered more acceptable when it allows the vessel to reach the unloading terminal at destination point 82 with unloading pressure and temperature close to those required by the unloading terminal.

[0133] Additionally or alternatively, the cost function also depends on whether tank management scheme 50 meets some or all of the following criteria:

[0134] - The pressure P of the gaseous substance contained in tank 3 is kept below the maximum safe pressure; the maximum safe pressure may be a predefined set value or even a variable value that depends on the environmental conditions encountered by the ship 1 along the predicted path 80, and the variable value may be low, for example, in the event of a storm or other potentially dangerous navigation conditions;

[0135] - The pressure P of the gaseous phase contained in tank 3 is kept above a minimum pressure; this minimum pressure may be a predetermined set value or even a variable value that depends on the amount of BOG required by engine 40, as estimated by flow model 63.

[0136] - The flow rate of BOG through the vapor phase treatment system 10 is kept below the threshold.

[0137] As previously stated, each of steps 401 to 404 is performed in the context of each evaluation in the evaluation S1, S2, ..., SN of the tank management scheme 50.

[0138] After evaluating S1, S2, ……, SN, method 300 proceeds to step 306, which involves verifying whether a stop criterion has been verified. In one embodiment, the stop criterion is a computation time criterion. In other words, if a pre-defined computation time is reached or exceeded, the stop criterion is considered verified. Alternatively, the stop criterion can be a criterion with the number of iterations.

[0139] In any case, if the stop criterion is not verified ( Figure 5B reference numeral “N” in step 306 in), method 300 proceeds to step 307, which involves selecting k tank management schemes 50 from the N tank management schemes 50 evaluated during the evaluation of S1, S2, ……, SN, where k < N. The k tank management schemes 50 selected in step 307 are the tank management schemes 50 that best minimize the cost function calculated in step 404. In an alternative embodiment, k is a pre-set quantity. In another alternative embodiment, k is not pre-set, and the k tank management schemes 50 selected are the tank management schemes 50 for which the cost function calculated in step 404 is below a threshold.

[0140] After step N, method 300 proceeds to step 308, which involves generating N new tank management schemes 50 based on the k tank management schemes 50 selected in step 307.

[0141] As described above, in this embodiment, method 300 uses the cost function calculated in step 404 as the objective function to implement an evolutionary algorithm. In a specific embodiment, the evolutionary algorithm is a genetic algorithm. Optimization algorithms using evolutionary algorithms are well-known. Step 305 then involves initializing the group considered by the evolutionary algorithm by randomly generating N tank management schemes 50, and step 308 involves generating N new tank management schemes 50 by crossing and mutating the tank management schemes selected in step 307.

[0142] Conversely, if the stop criterion is verified ( Figure 5B reference numeral “O” in step 306 in), method 300 proceeds to step 309, which involves selecting p tank management schemes 50 from the N tank management schemes 50 evaluated during the evaluation of S1, S2, ……, SN, where 1 ≤ p < N. The tank management schemes 50 selected in step 307 are the tank management schemes 50 that best minimize the cost function calculated in step 404. In an alternative embodiment, p is a pre-set quantity. In another alternative embodiment, p is not pre-set, and the p tank management schemes selected are the tank management schemes for which the cost function calculated in step 404 is below a threshold.

[0143] After step 309, method 300 proceeds to step 310, which involves, for example, displaying the tank management scheme selected in step 309 to the user on display device 41.

[0144] In a highly simplified alternative implementation, a single tank management scheme 50 is generated in step 305. Steps 401 to 404 are then performed on this single tank management scheme 50, and steps 306, 307, and 308 are omitted. Step 309 involves determining whether the tank management scheme 50 is acceptable based on the cost function calculated in step 404. If acceptable, the tank management scheme 50 is displayed in step 310.

[0145] In another simplified alternative implementation, multiple tank management schemes 50 are generated in step 308, but steps 306, 307, and 308 are omitted.

[0146] The steps of method 300 can be repeated at set intervals and / or when the communication interface 130 receives a new weather forecast. Alternatively, the steps of method 300 can be repeated at the user's command.

[0147] In any case, in step 310, method 300 causes at least one tank management scheme 50 to be displayed to the user, for example, on display device 41, based on the cost function calculated in step 404. Therefore, method 300 allows for decision assistance to be provided to the user.

[0148] As described above, a given tank management scheme 50 can define the average values ​​P1, P2, P3, P4, P5, P6, etc., of pressure P over consecutive time intervals d1, d2, d3, d4, d5, d6, etc. In a preferred embodiment, after step 309 and before step 310, one or more tank management schemes 50 selected in the steps can be post-processed to further display the minimum and maximum values ​​of pressure P over consecutive time intervals d1, d2, d3, d4, d5, d6, etc., to the user.

[0149] Figure 9 The diagram illustrates an example of a tank management scheme 50 for such post-processing. As shown in the diagram, the tank management scheme 50 defines a maximum value M1 and a minimum value m1 in addition to the average value P1 at interval d1, wherein the pressure P cannot be greater than the maximum value M1 or less than the minimum value m1. Similarly, at each interval d1, d2, d3, d4, d5, d6, etc., the tank management scheme 50 defines maximum values ​​M2, M3, M4, M5, M6, etc., and minimum values ​​m2, m3, m4, m5, m6, etc., respectively.

[0150] The maximum value M1 is calculated based on the average value P1 and one or more constraints associated with tank 3. Constraints associated with tank 3 may include safety constraints of tank 3, such as the maximum safe pressure of the tank. All or some of the constraints associated with tank 3 may be input by the user using acquisition device 42.

[0151] The maximum values ​​M2 to M6 are calculated in a similar manner based on the average values ​​P2 to P6 and one or more constraints associated with tank 3.

[0152] The minimum value m1 is calculated based on the average value P1 and the amount of gas to be removed from tank 3 during interval d1, estimated using flow model 63 in step 403.

[0153] The minimum values ​​m2 to m6 are calculated in a similar manner based on the average values ​​P2 to P6 and the amount of gas to be removed from tank 3 during the intervals d2 to d6.

[0154] In a simple alternative implementation, one or more tank management schemes 50 for such post-processing can be simply displayed to the user, along with minimum values ​​m1 to m6 and corresponding maximum values ​​M1 to M6. This allows for additional decision-making assistance by also displaying to the user a range of values, i.e., intervals [m1, M1], [m2, M2], etc., in which pressure P can vary within intervals d1, d2, etc.

[0155] As an alternative implementation, only the maximum values ​​M1 to M6 or the minimum values ​​m1 to m6 can be calculated and displayed.

[0156] In a particularly preferred alternative embodiment, one or more tank management schemes 50 in such subsequent processing may proceed to a further optimization step prior to step 310, which is used to determine the optimal value of pressure P at sub-intervals such as intervals d1, d2, etc.

[0157] Figure 10 The diagram illustrates an example of such an optimized tank management scheme 50A. As shown in the diagram, the time interval d1 is subdivided into multiple consecutive sub-time intervals i1, i2, i3, i4, i5, i6, etc. Specifically, each of the time intervals i1, i2, i3, i4, i5, i6, etc., can be equal to one hour. In addition to the average value P1, the minimum value m1, and the maximum value M1, the tank management scheme 50A also defines average values ​​Pi1, Pi2, Pi3, Pi4, Pi5, Pi6 for each of the sub-time intervals i1, i2, i3, i4, i5, i6. Similarly, and although for the sake of simplicity... Figure 10It is not shown in the figure, but the time intervals d2, d3, d4, d5, d6, etc. are also subdivided into multiple consecutive sub-time intervals, and the average value of pressure P is defined in each of these sub-intervals.

[0158] The average values ​​Pi1, Pi2, etc., are determined by an evolutionary algorithm using a cost function as the objective function. This cost function depends at least on the total amount of BOG generated in tank 3 during interval i1 and meets the following criteria:

[0159] - The pressure P of the gaseous substance contained in tank 3 is kept below the maximum value M1;

[0160] - The pressure P of the gaseous substance contained in tank 3 is maintained at a minimum of m1 or above;

[0161] - The flow rate of BOG through the vapor phase treatment system 10 is kept below the threshold.

[0162] This determination is made using steps similar to steps 401 to 404 to 306 to 308, and therefore will not be described in detail. One or more tank management schemes 50A are then presented to the user.

[0163] As described above, in step 304, the speed curve can be simply provided as input data; for example, the speed curve can be provided by the user using acquisition device 41. However, in step 304, it is preferable to determine the speed curve using an evolutionary algorithm with an objective function that depends on the total amount of BOG generated in tank 3 along the predicted path 80 and the difference between the required arrival time of ship 1 and the estimated arrival time of ship 1. The required arrival time of ship 1 can be input by the user using acquisition device 42.

[0164] In the context of this evolutionary algorithm, the velocity management scheme 550 is generated iteratively multiple times in a manner known per se, wherein each newly generated velocity management scheme 550 is generated based on multiple previously generated velocity management schemes 550 that optimally minimize the objective function.

[0165] Figure 11 This diagram illustrates an example of the speed management scheme 550. As shown in the diagram, when the predicted path 80 is divided into consecutive travel distance intervals y1, y2, y3, y4, y5, etc., the given speed management scheme 550 limits the evolution of the speed V of the ship 1 by defining the average values ​​V1, V2, V3, V4, V5, etc., of the speed V over each of the consecutive travel distance intervals y1, y2, y3, y4, y5, etc. Advantageously, the travel distance intervals y1, y2, y3, y4, y5, etc., can be defined with reference to points 81, 82, 83 of the predicted path 80.

[0166] Reference Figure 12 The steps for implementing the evolutionary algorithm will now be described.

[0167] During step 601, for example, multiple speed management schemes 550 are randomly generated.

[0168] After step 601, when M is the number of speed management schemes 550 generated in step 601, each speed management scheme 550 is evaluated M times (R1, R2, ..., RM). Although Figure 12 The diagram shows the evaluations of R1, R2, ..., RM as performed sequentially, but optionally, these evaluations can be performed simultaneously. Each evaluation of R1, R2, ..., RM includes steps 602, 603, 604, 605, and 606 as described below.

[0169] In step 602, the flow dynamics model of ship 1 is used to estimate the actual velocity curve of ship 1.

[0170] In step 603, the actual trajectory of ship 1 is estimated based on the actual velocity curve estimated in step 602 and using an ocean current model.

[0171] In step 604, at least one environmental parameter is determined based on a weather forecast along the actual trajectory of ship 1. Specifically, in a manner similar to step 303, central unit 110 uses communication interface 130 to obtain a weather forecast provided by a weather forecast provider, and central unit 110 determines at least one environmental parameter based on the weather forecast and the actual trajectory of ship 1 estimated in step 603.

[0172] In step 605, tank model 62 is used to estimate the curve of the amount of BOG generated in tank 3 along the actual trajectory of ship 1.

[0173] Following steps 601 to 605, in step 606, the objective function is calculated. The objective function is a real number that quantifies the acceptability of the speed management scheme 550. As described above, the objective function depends on the total amount of BOG generated in tank 3 along the predicted path 80 and the difference between the required arrival time of ship 1 and the estimated arrival time of ship 1 at its destination.

[0174] After evaluating R1, R2, ..., RM, the method proceeds to step 607, which involves verifying whether the stopping criterion has been verified. In one implementation, the stopping criterion is a computation time criterion. In other words, if a predetermined computation time is reached or exceeded, the stopping criterion is considered verified. Alternatively, the stopping criterion could be a criterion based on the number of iterations.

[0175] In any case, if the verification stop criterion ( Figure 12 the reference numeral "N" in step 607 of

[0176] is not verified), the method proceeds to step 608, which involves selecting k' speed management schemes 550 from the M speed management schemes 550 evaluated during the evaluation of R1, R2, ……, RM, where k' < M. The k' speed management schemes 550 selected in step 608 are the speed management schemes 550 that best minimize the objective function calculated in step 606. In an alternative embodiment, k' is a preset quantity. In another alternative embodiment, k' is not preset, and the k' speed management schemes 550 selected are the speed management schemes 550 for which the cost function calculated in step 608 is lower than a threshold.

[0176] After step 608, the method proceeds to step 609, which involves generating M new tank management schemes based on the k' speed management schemes 550 selected in step 608.

[0177] In a particular embodiment, the evolutionary algorithm is a genetic algorithm. Step 601 then involves initializing the group considered by the evolutionary algorithm by randomly generating M speed management schemes 550, and step 609 involves generating M new speed management schemes 550 by performing crossover and mutation on the speed management schemes selected in step 608.

[0178] Conversely, if the verification stop criterion ( Figure 12 the reference numeral "O" in step 607 of

[0179] Figure 2 is verified), the method proceeds to step 610, which involves selecting the speed management scheme 550 that best minimizes the objective function calculated in step 606. Finally, in step 611, this speed management scheme 550 is designated as the speed curve of vessel 1 along the predicted path, after which steps 305 to 310 and the evaluation of S1, S2, ……, SN can be implemented as already described above.

[0179] Figure 2 The configuration of the vapor phase treatment system 10 shown in

[0180] is only an example. Other configurations are possible.

[0180] In some alternative embodiments not shown, vessel 1 may include more than one engine 40. In this case, the engine model 61 is capable of estimating the power to be output from each engine 40 based on the speed setpoint of vessel 1, at least one environmental parameter, and optionally the heading of vessel 1.

[0181] As described above with respect to Figure 2As described, engine 40 can provide the power required to propel the vessel 1, as well as the power required to electrically drive other equipment on the vessel 1 (commonly referred to as "hotel loads"). However, as Figure 13 As shown, the vessel 1 may include an auxiliary engine 49 for supplying power to this "hotel load". Like engine 40, the auxiliary engine 49 can consume BOG from either the BOG distribution unit 20 or the heater 60. In this case, engine model 61 can estimate the power required to be output from engine 40 and auxiliary engine 49 based on the vessel 1's speed setpoint, at least one environmental parameter, and optionally the vessel 1's heading. It should also be noted that, as an alternative implementation, multiple auxiliary engines 49 may be provided; in this case, engine model 61 can similarly estimate the power required to be output from one or more engines 40 and auxiliary engines 49.

[0182] Figure 14 Another alternative embodiment of the vapor phase processing system 10 is shown, which includes a reliquefaction facility 75.

[0183] In a manner known per se, the reliquefaction facility 75 is capable of receiving BOG from the BOG distribution unit 20, reliquefying the BOG, and returning the BOG to tanks 3, 4, 5, and 6. Such a reliquefaction facility 75 is known and therefore will not be described in detail herein.

[0184] It should be noted that, although in Figure 14 Although not shown, the power (e.g., electricity) required to operate the reliquefaction facility 75 may be provided by engine 40 or even by auxiliary engine 49. It should also be noted that in some alternative embodiments (not shown), the reliquefaction facility 75 may send the reliquefied BOG only to one or some of tanks 3, 4, 5, and 6.

[0185] Of course, in this alternative implementation, flow model 63 takes into account the presence of reliquefaction facility 75.

[0186] More specifically, the flow model 63 can estimate the following: the amount of gas to be removed from tank 3 by the vapor phase treatment subsystem 13, the amount of BOG to be burned in GCU 30, and the amount of BOG to be sent to the reliquefaction facility 75. More specifically, the flow model 63 estimates the amount of BOG to be supplied to engine 40 based on the power estimated by model 61, and compares this amount of BOG to be supplied to engine 40 with the amount of BOG generated in tank 3 estimated by tank model 62. If the amount of BOG to be supplied to engine 40 is greater than the amount of BOG generated in tank 3, the flow model 63 determines that the difference between these two amounts will be removed from tank 3 by the vapor phase treatment subsystem 13. Conversely, if the amount of BOG supplied to engine 40 is less than the amount of BOG produced in tank 3, flow model 63 determines that the difference between the two amounts will preferably be sent to reliquefaction facility 75 and burned in GCU 30 only if the reliquefaction capacity of reliquefaction facility 75 is insufficient.

[0187] It is also understood that, in this alternative embodiment, in step 403, based on the curve of the power required to be output from engine 40 estimated in step 401 and the curve of the amount of BOG generated in tank 3 estimated in step 402, flow model 63 is used to estimate the curve of the amount of gas to be taken out of tank 3, the curve of the amount of BOG to be burned in GCU 30, and the curve of the amount of BOG to be sent to reliquefaction facility 75.

[0188] Figure 15 Another alternative embodiment of the vapor phase processing system 10 is shown, which further includes a subcooler 77.

[0189] In a manner known per se, the subcooler 77 is capable of receiving a portion of the liquid phase 3L contained in the tank 3 from the vapor phase processing subsystem 13 and reducing the temperature of that portion (e.g., by 5 to 10 degrees Celsius, i.e., approximately -162°C to approximately -167°C to -172°C in the case of LNG at atmospheric pressure). The subcooler 77 is also capable of returning such subcooled liquid phase to the tank 3, either directly to the liquid phase 3L via a known mixing nozzle 79, or indirectly by spraying it into the vapor phase 3G in droplet form via a known spraying ramp 78.

[0190] It should be noted that, in Figure 15As not shown, the power (e.g., electricity) required to operate the subcooler 77 may be provided by engine 40 or even by auxiliary engine 49. It should be noted that in some alternative embodiments (not shown), the subcooler 77 is capable of removing liquid phase from multiple tanks 3, 4, 5, 6, or even all of them, and returning the removed liquid phase to the respective tank in a subcooled state.

[0191] Of course, in this alternative implementation, flow model 63 takes into account the presence of subcooler 77.

[0192] More specifically, flow model 63 can estimate the following: the amount of gas to be removed from tank 3 by vapor phase treatment subsystem 13, the amount of BOG to be burned in GCU 30, and the amount of liquid phase to be sent to subcooler 77. More specifically, flow model 63 estimates the amount of BOG to be supplied to engine 40 based on the power estimated by model 61, and compares this amount of BOG to be supplied to engine 40 with the amount of BOG generated in tank 3 estimated by tank model 62. If the amount of BOG to be supplied to engine 40 is greater than the amount of BOG generated in tank 3, flow model 63 determines that the difference between these two amounts will be removed from tank 3 by vapor phase treatment subsystem 13. Conversely, if the amount of BOG supplied to engine 40 is less than the amount of BOG produced in tank 3, flow model 63 determines that the difference between the two amounts will decrease over time by preferably operating subcooler 77, and any excess BOG must be burned in GCU 30 only if the subcooling capacity of subcooler 77 is insufficient.

[0193] It is also clearly understood that, in this alternative embodiment, in step 403, based on the curve of the power required to be output from engine 40 estimated in step 401 and the curve of the amount of BOG generated in tank 3 estimated in step 402, flow model 63 is used to estimate the curve of the amount of gas to be taken out of tank 3, the curve of the amount of BOG to be burned in GCU 30, and the curve of the amount of liquid phase to be taken out of tank 3 and sent to subcooler 77.

[0194] Finally, in some alternative embodiments not shown, it should be noted that the subcooler 77 is capable of removing liquid phase from one of the tanks 3, 4, 5, and 6 and returning the removed liquid phase in a subcooled state to one or more of the tanks 3, 4, 5, and 6. In this case, when the management assistance method 300 is implemented simultaneously for multiple tanks or each of the tanks 3, 4, 5, and 6 as described above, the flow rate model 63 associated with a given tank also estimates the amount of cooled liquid phase to be received from the subcooler 77 originating from one or more other tanks.

[0195] Although the invention has been described with reference to several specific embodiments, it is apparent that the invention is by no means limited thereto, and that the invention includes all the described technical equivalents of the apparatus and combinations thereof if such technical equivalents or combinations thereof fall within the scope of the invention.

[0196] The use of the verbs “comprising” or “including” and their variations does not exclude the presence of elements or steps other than those described in the claims.

[0197] In the claims, any reference numerals between parentheses shall not be construed as limiting the claims.

Claims

1. A computer-implemented method for assisting in the management of a ship (1) for transporting liquefied gas, the ship including a vapor phase handling system (10) and at least one tank (3) configured to contain the liquefied gas, the vapor phase handling system (10) being capable of sending evaporated gas exiting the tank (3) to a propulsion engine (40) of the ship or to a gas combustion unit (30) on the ship, the vapor phase handling system (10) also being capable of removing a portion of liquid phase (3L) contained in the tank (3) and evaporating the portion to send the portion to the propulsion engine (40), the method comprising: - Provide (301) the initial state of the tank, the initial state of the tank including the initial pressure of the gas phase (3G) contained in the tank (3) and the initial temperature of the liquid phase (3L) contained in the tank (3); - Provide (302) the predicted path (80) of the ship (1); - Determine at least one environmental parameter of the ship along the predicted path (80) based on weather forecast (303). - Determine (304) the speed curve of the ship (1) along the predicted path (80); - Generate (305) tank management schemes (50, 50A), which define the evolution of the pressure of the gaseous substance (3G) contained in the tank during the predicted path (80), and based on the tank management schemes thus generated: a) Estimate (401) the curve of the power required to be output from the propulsion engine (40) along the predicted path (80) based on the speed curve of the ship and the at least one environmental parameter. b) Estimate (402) the curve of the amount of evaporated gas generated in the tank (3) along the predicted path; c) Based on the curves of the power required to be output from the propulsion engine along the predicted path and the amount of evaporated gas generated in the tank along the predicted path, estimate (403) the curves of the amount of gas to be taken out of the tank and the amount of evaporated gas to be burned in the gas combustion unit (30); and d) Calculate the cost function (404), which depends at least on the total amount of evaporated gas generated in the tank along the predicted path (80); as well as - Display the tank management scheme (50, 50A) to the user based on the calculated cost function (310).

2. The method according to claim 1, wherein, Generate multiple tank management schemes (50, 50A), and perform steps a) to d) for each tank management scheme, wherein at least one of the tank management schemes is displayed to the user based on the calculated cost function of the tank management scheme.

3. The method according to claim 2, wherein, Before displaying (310) at least one of the tank management schemes (50, 50A) to the user, the tank management scheme is regenerated iteratively multiple times by a first evolutionary algorithm, which uses the cost function as a first objective function.

4. The method according to any one of claims 1 to 3, wherein, The predicted path (80) includes path steps (84), each path step (84) being defined by two waypoints (83) and a course to be followed between the two waypoints, and wherein, in step a), the curve of the power required to be output from the propulsion engine (40) along the predicted path (80) is estimated based on the course to be followed, the speed curve of the ship, and the at least one environmental parameter.

5. The method according to any one of claims 1 to 4, wherein, The speed curve of the ship is determined by a second evolutionary algorithm using a second objective function based on the predicted path (80) and the at least one environmental parameter, the second objective function depending on the total amount of vaporized gas generated in the tank along the predicted path (80) and the difference between the required time to reach the destination and the estimated time to reach the destination.

6. The method according to any one of claims 1 to 5, wherein, Providing (302) the predicted path (80) of the ship includes providing the unloading pressure and / or unloading temperature required at the unloading terminal at the destination of the ship, and wherein, in step d), the cost function also depends on the difference between the pressure of the gaseous phase (3G) contained in the tank (3) at the end of the predicted path (80) and the unloading pressure and / or the difference between the temperature of the liquid phase (3L) contained in the tank (3) at the end of the predicted path (80) and the unloading temperature.

7. The method according to any one of claims 1 to 6, wherein, In step a), a first statistical model is used to estimate the curve of the power required to be output from the propulsion engine along the predicted path. The first statistical model is capable of estimating the power required to be output from the propulsion engine (40) based at least on the ship speed setpoint and the at least one environmental parameter. The first statistical model is trained by a supervised machine learning method.

8. The method according to any one of claims 1 to 7, wherein, The above step b) involving estimating (402) the amount of vaporized gas generated in the tank (3) along the predicted path (80) includes: estimating a first amount of vaporized gas generated in the tank based on the current pressure of the gaseous phase (3G) contained in the tank and the current temperature of the liquid phase (3L) contained in the tank; and estimating a second amount of vaporized gas generated in the tank based on the current pressure of the gaseous phase (3G) contained in the tank and the at least one environmental parameter.

9. The method according to claim 8, wherein, The second amount of evaporated gas generated in the tank (3) is estimated using a second statistical model trained by a supervised machine learning method.

10. The method according to any one of claims 1 to 9, wherein, The initial state includes the initial composition of the liquefied gas contained in the tank, and step b) which involves estimating (402) the amount of vaporized gas generated in the tank (3) along the predicted path (80) further includes estimating the composition of the vaporized gas generated in the tank.

11. The method according to any one of claims 1 to 10, wherein, Environmental parameters include at least one of the following: current direction, current speed, wind speed, wind direction, mean wave height, wave direction, and wave period.

12. The method according to any one of claims 1 to 11, wherein, The vapor phase processing system (10) further includes a reliquefaction facility (75) capable of reliquefying the evaporated gas leaving the tank (3) and returning the reliquefied evaporated gas back to the tank, wherein step c) above includes: estimating (403) the curves of the amount of gas to be taken out of the tank, the amount of evaporated gas to be burned in the gas combustion unit (30), and the amount of evaporated gas to be sent to the reliquefaction facility (75) based on the curves of the power required to be output from the propulsion engine along the predicted path and the amount of evaporated gas generated in the tank along the predicted path.

13. The method according to any one of claims 1 to 12, wherein, The vapor phase processing system (10) further includes a subcooler (77) capable of subcooling a portion of the liquid phase (3L) contained in the tank (3) and returning the so-called subcooled portion to the tank, wherein step c) comprises: estimating (403) the curves of the amount of gas to be taken out of the tank, the amount of evaporating gas to be burned in the gas combustion unit (30), and the amount of liquid phase to be taken out of the tank and sent to the subcooler (77) based on the curves of the power required to be output from the propulsion engine along the predicted path and the amount of evaporating gas generated in the tank along the predicted path.

14. The method according to claim 1, wherein, The steps of the method are repeated at fixed intervals and / or when a new weather forecast is received.

15. A management assistance system (100, 200) for assisting in the management of a ship used for transporting liquefied gases, the ship (1) including a vapor phase handling system (10) and at least one tank (3) configured to contain liquefied gases, the vapor phase handling system (10) being capable of sending evaporated gas exiting the tank (3) to a propulsion engine (40) of the ship or to a gas combustion unit (30) on the ship, the vapor phase handling system (10) also being capable of removing a portion of liquid phase (3L) contained in the tank (3) and evaporating the portion to send the portion to the propulsion engine (40), the management assistance system (100, 200) comprising: - Acquisition unit (42), the acquisition unit (42) is configured to acquire the initial state of the tank and the predicted path (80) of the ship, the initial state of the tank including the initial pressure of the gas phase (3G) contained in the tank (3) and the initial temperature of the liquid phase (3L) contained in the tank (3); - A first communication unit (130) configured to obtain a weather forecast from a weather forecast provider; as well as - A calculation unit (110) configured to: determine (304) the velocity curve of the ship along the predicted path (80); generate (305) a tank management scheme (50, 50A) that defines the evolution of the pressure of the gaseous phase (3G) contained in the tank (3) along the predicted path (80); and, based on the tank management scheme thus generated, the calculation unit (110) is configured to: a) Estimate (401) the curve of the power required to be output from the propulsion engine (40) along the predicted path (80) based on the speed curve of the ship and the at least one environmental parameter. b) Estimate (402) the curve of the amount of evaporated gas generated in the tank (3) along the predicted path; c) Based on the curves of the power required to be output from the propulsion engine along the predicted path and the curves of the amount of evaporated gas generated in the tank along the predicted path, estimate (403) the curves of the amount of gas to be taken out of the tank and the curves of the amount of evaporated gas to be burned in the gas combustion unit (30). d) Calculate a cost function (404) that depends at least on the total amount of evaporated gas generated in the tank along the predicted path (80); and includes - Display unit (41), which is configured to display the tank management scheme (50, 50A) to the user according to the calculated cost function.

16. The management support system (100, 200) according to claim 15, wherein, The predicted path (80) includes path steps (84), each path step (84) being defined by two waypoints (83) and a course to be followed between the two waypoints, and wherein the calculation unit (110) is configured to: a) estimate (401) a curve of the power required to be output from the propulsion engine (40) along the predicted path (80) based on the course to be followed, the speed curve of the ship, and the at least one environmental parameter.

17. The management support system according to claim 15 or 16, wherein, The acquisition unit (42) and the display unit (41) are located on the ship, the first communication unit (130) and the computing unit (110) are located in the ground station (1000), and wherein the management assistance system further includes a second communication unit (230) configured to connect the acquisition unit (42) and the computing unit (110).

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