Management support method and system for liquefied gas transport vessels that consume vaporized gas for propulsion

Abstract

The present invention relates to a method for supporting the management of a ship, comprising at least one tank (3) containing liquefied gas and a gas phase treatment system (10) capable of delivering vaporized gas released from the tank to a propulsion engine (40) of the ship or to a gas combustion unit (30) mounted on the ship, and capable of extracting a portion of the liquid phase contained in the tank, vaporizing this portion and delivering it to the propulsion engine (40). The method includes a step of generating (305) at least one tank management scenario defining the change in pressure of the gas phase contained in the tank along the ship's trajectory, a step of calculating (404) a cost function that depends at least on the total amount of vaporized gas generated in the tank along the trajectory, and a step of displaying the tank management scenario to a user based on the calculated cost function.

Description

Technical field

[0001] The present invention relates to the field of liquefied gas transport vessels of the type that consume boil-off gas for propulsion. More specifically, the present invention proposes such a ship management support method and system. [Background technology]

[0002] Closed and insulated tanks are commonly used to transport liquefied natural gas (LNG) at about -162°C under atmospheric pressure. These tanks may be intended for transporting liquefied gases. Many liquefied gases can also be envisaged, in particular methane, ethane, propane, butane, ammonia, dihydrogen or ethylene.

[0003] Marine tanks may be single or double sealed membrane tanks allowing transport under atmospheric pressure. These sealing membranes are typically made from stainless steel sheet or Invar sheet. The membrane is usually in direct contact with the liquefied gas.

[0004] Liquefied gases are transported at temperatures significantly below ambient temperature, so when such gases are transported by a ship equipped with one or more such tanks, despite insulating the tanks. There is a natural tendency for it to gradually heat up. The gas generated by this heating is generally called boil-off gas (BOG). As an example, when LNG is transported at about -162°C under atmospheric pressure, it is possible to observe the generation of BOG per day in an amount of about 0.05% to 0.15% of the volume of LNG initially stored in the tank.

[0005] In order to utilize BOG, it is known that all or part of the BOG is used for propulsion of the ship by delivering the BOG to the ship's propulsion engine which can consume the BOG. The vessel is further equipped with a gas combustion unit (GCU) that burns excess BOG that could not be consumed by the vessel's propulsion engines, typically in a known manner.

[0006] BOG that is burned in the GCU along the path taken by the vessel will represent a loss as it will not be delivered to the vessel and will be consumed without any benefit. Therefore, it is desirable to minimize the amount of BOG loss along a given path of a vessel. Moreover, even utilizing BOG delivered to the vessel's propulsion engine would also represent a reduction in the amount delivered to the vessel's destination.

[0007] Additionally, a solution to assist ship management that allows limiting the total amount of BOG generated within the tank to limit the amount of BOG burned within the GCU and the amount of BOG delivered to the vessel's propulsion engine. measures are needed. [Summary of the invention] [Problem to be solved by the invention]

[0008] One idea behind the invention is to use weather forecasts along with the ship's predicted path and ship operating model to estimate at least the total amount of boil-off gas generated in the ship's tanks. Another idea behind the invention is to carry out this estimation for at least one and preferably several tank management scenarios that define the change in pressure of the gas phase contained within the tank along the predicted path. It is to be. Yet another idea behind the invention is to present at least one of the tank management scenarios to the user as a function of a cost function that depends at least on the total amount of boil-off gas generated in the tank along the predicted path. It is to display. [Means to solve the problem]

[0009] According to one embodiment, the present invention provides a computer-implemented method for assisting in the management of a liquefied gas transport vessel, the vessel having at least one tank configured to contain liquefied gas. and a gas phase treatment system, the gas phase treatment system being capable of delivering the boil-off gas discharged from the tank to a propulsion engine of the vessel or a gas combustion unit onboard the vessel; further comprising: a gas phase processing system capable of removing a portion of the liquid phase contained within the tank, vaporizing the portion and delivering the portion to the propulsion engine; - providing an initial state of the tank, the initial state of the tank comprising an initial pressure of a gas phase contained within the tank and an initial temperature of a liquid phase contained within the tank; - providing a predicted path for the vessel; - determining at least one environmental parameter of the vessel along the predicted route based on the weather forecast; - determining a speed curve of the vessel along the predicted route; - generating a tank management scenario defining a change in the pressure of the gas phase contained within the tank during the course of the predicted path, based on the tank management scenario thus generated; a) estimating a curve of power required to be output from the propulsion engine along the predicted path as a function of the speed curve of the vessel and the at least one environmental parameter; b) estimating a curve of the amount of boil-off gas generated in the tank along the predicted path; c) estimating a curve for the amount of gas to be extracted from the tank and a curve for the amount of boil-off gas to be combusted in the gas combustion unit based on the required power curve and the curve for the amount of boil-off gas generated; d) generating a tank management scenario, the step of calculating a cost function that depends at least on the total amount of boil-off gas generated in the tank along the predicted path; - displaying the tank management scenario to the user as a function of the calculated cost function; including.

[0010] These features allow tank management scenarios to be evaluated by calculating cost functions and displayed to the user for the purpose of providing decision support.

[0011] According to some embodiments, such methods can include one or more of the following features.

[0012] According to one embodiment, a plurality of tank management scenarios are generated, steps a) to d) are performed for each tank management scenario, and at least one of the tank management scenarios is Displayed to the user as a function of the calculated cost function.

[0013] Therefore, a considerable number of tank management scenarios can be evaluated by calculating the cost function, and the user selects one or more tank management scenarios that best minimize the cost function, with the aim of providing decision support. can be displayed.

[0014] According to one embodiment, before the step of displaying at least one of the tank management scenarios to the user, the tank management scenario is configured using a first evolutionary algorithm using a cost function as a first objective function. is regenerated repeatedly by

[0015] The term "evolutionary algorithm" is understood to mean a method, typically implemented by a computer, in which a group of solutions is generated and each solution is then evaluated by an objective function to minimize the objective function. Some of the solutions are selected, a new group of solutions is generated from the solutions thus selected, and these steps are repeated unless a stopping criterion is verified. Within the scope of the present invention, each tank management scenario is a solution, and the cost function serves as the objective function of the evolutionary algorithm.

[0016] Various types of evolutionary algorithms are known per se. In one embodiment, the evolutionary algorithm is a genetic algorithm. Evolutionary algorithms are particularly well suited to optimization problems.

[0017] As mentioned above, evolutionary algorithms need to use stopping criteria to decide when to stop searching for more optimal solutions. According to one embodiment, the stopping criterion is a computation time criterion that ensures that the method displays one or more optimized tank management scenarios to the user at the end of a given computation time.

[0018] According to one embodiment, the predicted path of the vessel is divided into successive time intervals, and the one or each tank scenario is defined as the average value of the pressure of the gas phase contained within the tank during each of said successive time intervals. Define.

[0019] According to one embodiment, the at least one tank management scenario displayed to the user comprises: during each of the successive time intervals, the minimum pressure of the gas phase contained within the tank and / or the minimum pressure of the gas phase contained within the tank. Displayed together with the maximum pressure of the gas phase involved.

[0020] According to one embodiment, the predicted path includes path steps each defined by two waypoints and a direction of travel between the two waypoints, and in step a), the predicted path includes: A curve of the power that needs to be output is estimated as a function of the direction of travel, the speed curve of the vessel and the at least one environmental parameter.

[0021] In this way, the evaluation of tank management scenarios also takes into account the influence of the vessel's heading on the power required to be output from the propulsion engines. Indeed, as an example, depending on the angle between the ship's direction of travel and the current, wind and wave directions, this power can be larger or smaller.

[0022] According to one embodiment, the speed curve of the vessel is determined between the total amount of boil-off gas generated in the tank along the predicted path and the required time of arrival at the destination and the estimated time of arrival at the destination. is determined based on the predicted path and the at least one environmental parameter by a second evolutionary algorithm using a second objective function that depends on the difference between the predicted path and the at least one environmental parameter.

[0023] In this way, the estimation of the power required to be output from the propulsion engine takes into account the time required for the arrival of the vessel, which time can be specified as an input parameter by the user.

[0024] Terminals for discharging liquefied gases systematically require that the tanks have a given discharging pressure and / or discharging temperature. According to one embodiment, the step of providing a predicted route for the vessel includes the step of providing the discharge pressure and / or discharge temperature required by the discharge terminal of the vessel's destination, and in step d) the cost function is , the difference between the pressure of the gas phase contained in the tank at the end of the predicted route and the discharge pressure, and / or the temperature of the liquid phase contained in the tank at the end of the predicted route and the discharge temperature. Further depends on the difference between.

[0025] In this way, tank management scenarios become even more acceptable if the unloading terminal located at the destination point is able to reach unloading pressures and / or unloading temperatures that approximate those required by the unloading terminal. It is considered to be.

[0026] According to one embodiment, in step a) the required power curve is estimated using a first statistical model, the first statistical model being a function of at least the vessel speed set point and the at least one environmental parameter. The first statistical model is trained by supervised machine learning techniques.

[0027] The term "supervised machine learning method" or "supervised learning" is understood to mean a machine learning method that involves learning a prediction function based on annotated examples. That is, in the supervised machine learning method, a predictive model can be constructed from a plurality of examples whose responses to be predicted are known. Since supervised machine learning techniques are typically performed by a computer, training of the first statistical model is typically performed by a computer.

[0028] Of course, in a sentence or feature that states "train a statistical model using supervised machine learning techniques on a set of test data," the "set of test data" is also derived from "real-world" activities. It will be appreciated that the data may include or consist of data acquired or recorded on vessels traveling to and from transport equipment and users of liquefied gases.

[0029] According to one embodiment, step b) above comprising the step of estimating a curve of the amount of boil-off gas generated in the ship along said predicted path is based on the current pressure of the gas phase contained in the ship and estimating a first amount of boil-off gas generated in the vessel as a function of a current temperature of a liquid phase contained in the tank; and a current pressure of a gas phase contained in the tank and said at least one environmental parameter. and estimating the amount of second boil-off gas generated in the tank as a function of the amount of boil-off gas generated in the tank.

[0030] Therefore, the step of estimating the amount of boil-off gas generated within the tank is based on the generation of boil-off gas associated with the coexistence of liquid and gas phases within the tank, and the agitation of the liquid phase due to the movements experienced by the vessel. Take into account both the generation of boil-off gas caused by

[0031] According to one embodiment, the second amount of boil-off gas generated in the tank is estimated using a second statistical model, and the second statistical model is trained by a supervised machine learning technique.

[0032] According to one embodiment, the initial conditions include an initial composition of the liquefied gas contained in the tank, step b) comprising estimating a curve of the amount of boil-off gas generated in the tank along said predicted path. The method further includes estimating a composition of boil-off gas generated within the tank.

[0033] Since a liquefied gas cargo is almost never chemically pure, this cargo is usually a mixture of several liquefied gases, one primarily by weight and the other(s) in the form of impurities. exist. However, the initial composition of the cargo is typically indicated by the source of the liquefied gas when it is introduced into the tank. By indicating the initial composition of the liquefied gas and estimating the composition of the boil-off gas generated in the tank, we can determine that the composition of the boil-off gas follows the predicted path due to the different vaporization characteristics of the gases forming the liquefied gas cargo. This takes into account the fact that the

[0034] According to one embodiment, the environmental parameters include at least one of current direction, current speed, wind speed, wind direction, average wave height, wave direction, and wave period.

[0035] According to one embodiment, the gas phase treatment system further comprises a reliquefaction plant capable of reliquefying the boil-off gas discharged from the tank and returning the boil-off gas thus reliquefied to the tank, the above-mentioned Step c) is based on the required power curve and the boil-off gas production amount curve to determine a curve for the amount of gas to be taken out from the tank, a curve for the amount of boil-off gas to be combusted in the gas combustion unit, and a curve for the amount of boil-off gas to be burned in the reliquefaction plant. and a curve of the amount of boil-off gas to be delivered to the boil-off gas.

[0036] According to one embodiment, the gas phase treatment system further includes a subcooler capable of supercooling a part of the liquid phase contained within the vessel and thus returning the supercooled part to the tank. In step c) above, a curve for the amount of gas to be taken out from the tank and a curve for the amount of boil-off gas to be burned in the gas combustion unit, based on the required power curve and the curve for the amount of boil-off gas generated, and a curve for the amount of liquid phase to be removed from the tank and delivered to the subcooler.

[0037] According to one embodiment, these steps of the method are repeated at regular intervals and / or upon receiving a new weather forecast.

[0038] Therefore, the method periodically re-evaluates the tank management scenario as the vessel progresses along its predicted path and as new weather forecasts are obtained. Additionally or alternatively, the steps of the method can be repeated in response to instructions by a user.

[0039] According to one embodiment, the invention further provides a management support system for a liquefied gas transport vessel, the vessel having at least one tank configured to contain liquefied gas and a gas phase treatment vessel. The system, wherein the gas phase treatment system is capable of delivering boil-off gas discharged from the tank to a propulsion engine of the vessel or a gas combustion unit onboard the vessel, the gas phase treatment system further comprising: This management support system includes a gas phase processing system that can extract a part of the liquid phase contained in the engine, vaporize the part, and deliver it to the propulsion engine. - an acquisition unit configured to obtain an initial state of the tank and a predicted path of the vessel, wherein said initial state of the tank includes an initial pressure of a gas phase contained within the tank and an initial pressure of a liquid phase contained within the tank; an acquisition unit, including an initial temperature; - a first communication unit configured to obtain a weather forecast from a weather forecast provider; - a calculation unit configured to determine a speed curve of the vessel along said predicted path and to generate a tank management scenario defining a change in the pressure of the gas phase contained within the tank along said predicted path; , the calculation unit calculates, based on the tank management scenario generated in this way, a) estimating a curve of the power required to be output from the propulsion engine along the predicted path as a function of the speed curve of the vessel and the at least one environmental parameter; b) Estimate the curve of the amount of boil-off gas generated in the tank along the predicted path, c) estimate a curve of the amount of gas to be taken out from the tank and a curve of the amount of boil-off gas to be combusted in the gas combustion unit based on the required power curve and the curve of the amount of boil-off gas generated; d) a calculation unit configured to calculate a cost function that is at least dependent on the total amount of boil-off gas generated in the tank along the predicted path; - a display unit configured to display tank management scenarios to a user as a function of a calculated cost function; Equipped with.

[0040] Such a system would offer the same advantages as the methods described above.

[0041] According to one embodiment, the predicted path includes path steps each defined by two waypoints and a direction of travel between the two waypoints, and the calculation unit includes: a) a propulsion engine along the predicted path; The vessel is configured to estimate a curve of power required to be output from the vessel as a function of the direction of travel, the speed curve of the vessel, and the at least one environmental parameter.

[0042] According to one embodiment, the acquisition unit and the display unit are mounted on board the ship, the first communication unit and the calculation unit are located at a ground station, and the system is configured to connect the acquisition unit and the calculation unit. further comprising a second communication unit configured. [Brief explanation of drawings]

[0043] The present invention will be better understood through the following description of some specific embodiments of the invention, given by way of non-limiting illustration only, with reference to the accompanying drawings, and further objects, details, features and advantages. will become clearer.

[0044]

Figure 1

[0045]

Figure 2

[0046] [Figure 3] 2 is a block diagram of the ship management support system of FIG. 1. FIG.

[0047] [Figure 4] 4 is a block diagram of the management support system for the vessel of FIG. 1 according to an alternative embodiment of FIG. 3; FIG.

[0048] [Figure 5A] 4 is a block diagram showing a first step of the ship management support method of FIG. 1, which can be implemented by the management support system of FIG. 3. FIG.

[0049] [Figure 5B] 5B is a block diagram illustrating future steps of the method of FIG. 5A. FIG.

[0050] [Figure 6] FIG. 2 is a diagram showing an example of a predicted route of a ship for explanation.

[0051] [Figure 7] 4 is a graph showing, for explanation, an example of a tank management scenario generated by the management support system of FIG. 3.

[0052] [Figure 8] 4 is a block diagram illustrating a model used by the management support system of FIG. 3 to evaluate a given tank management scenario; FIG.

[0053] [Figure 9] 8 is a graph illustrating an example of a tank management scenario similar to FIG. 7, with associated minimum and maximum pressure values ​​within the tank; FIG.

[0054] [Figure 10] 8 is a graph illustrating an example of a tank management scenario similar to FIG. 7, subdivided into sub-time intervals; FIG.

[0055] [Figure 11] 4 is a graph illustrating an example of a speed management scenario generated by the management support system of FIG. 3 in some embodiments; FIG.

[0056] [Figure 12] 4 is a block diagram illustrating a model used by the management support system of FIG. 3 to evaluate a given rate management scenario; FIG.

[0057] [Figure 13] FIG. 3 is a block diagram similar to that of FIG. 2 illustrating an alternative embodiment of a gas phase processing system.

[0058] [Figure 14] FIG. 3 is a block diagram similar to that of FIG. 2 illustrating another alternative embodiment of a gas phase processing system.

[0059] [Figure 15] FIG. 3 is a block diagram similar to that of FIG. 2 illustrating yet another alternative embodiment of a gas phase processing system. [Form for carrying out the invention]

[0060] The following embodiments are described with respect to a vessel with a double hull forming a support structure in which a plurality of closed and insulated tanks are arranged. In such a support structure, the tank has, for example, a polyhedral shape such as a prismatic shape.

[0061] Such a sealed and insulated tank is provided, for example, for transporting liquefied gas. Since liquefied gas is stored and transported at low temperatures in such tanks, insulated tank walls are required to maintain the liquefied gas at that temperature.

[0062] Such a sealed insulated tank further comprises an insulating barrier fixed to the double hull of the vessel and supporting at least one sealing membrane. By way of example, such tanks may be manufactured according to the technology sold under trademarks such as Mark III® or NO96® by the applicant.

[0063] FIG. 1 shows a ship 1 comprising four sealed and insulated tanks 2. The four tanks 2 can have the same or different filling status.

[0064] FIG. 2 is a block diagram schematically showing four tanks 3, 4, 5, 6 of the ship of FIG. 1. As shown in FIG. 2, the ship 1 further includes a gas phase processing system 10, an engine 40, and a gas combustion unit (GCU) 30.

[0065] The engine 40 provides the power necessary for propulsion of the vessel 1 and, in an alternative embodiment, provides the power necessary for propulsion of the vessel 1 and other equipment of the vessel 1 (commonly referred to as a "hotel load"). It can provide both the power needed to power the vehicle electrically. In a manner known per se, the engine 40 is intended to vaporize liquefied gas which may originate from boil-off gas (BOG) discharged from the tanks 3, 4, 5, 6 or which is withdrawn from the tanks. Gas phase gas that may be generated from the heater 60 can be consumed. Since such engines 40 are known per se, no further details are given here.

[0066] Furthermore, in a manner known per se, the GCU 30 can burn off any surplus BOG that cannot be consumed by the engine 40. Since such GCUs 30 are known per se, they will not be described in detail here.

[0067] Gas phase treatment system 10 can deliver BOG released from each tank 3, 4, 5, 6 to engine 40 or GCU 30, as required. To this end, the gas phase treatment system 10 includes a gas phase treatment subsystem 13, 14, 15, 16 for each of the tanks 3, 4, 5, 6 and these subsystems 13, 14, 15, 16 to allow BOG to be delivered to the engine 40 or GCU 30 as required. Since such gas phase treatment systems and subsystems are known per se, they will not be described in detail here.

[0068] Furthermore, in a manner known per se, each subsystem 13, 14, 15, 16 also removes, if desired, a portion of the liquid phase contained in the corresponding tank 3, 4, 5, 6 and this The portion can be delivered to heater 60 for vaporization.

[0069] Although ship 1 is shown with four tanks, the number of tanks that ship 1 has may vary, in particular a single tank, or even any number of tanks. This is clearly understood.

[0070] Although the management support method 300 will be described below with reference to tank 3 for convenience, it is understood that the management support method 300 can be performed on several tanks 3, 4, 5, 6, or on each of these simultaneously. is understood.

[0071] FIG. 3 shows an example of the management support system 100 installed on the ship 1. The management support system 100 comprises a central unit 110 connected to a communication interface 130, a human-machine interface 140, and a database 150.

[0072] Communication interface 130 allows central unit 110 to communicate with remote devices, for example, to obtain weather data, vessel position data, and the like.

[0073] Human-machine interface 140 includes display means 41. As described below, the display means 41 allows one or more tank management scenarios 50 to be displayed to the user.

[0074] As described below, the human-machine interface 140 further includes acquisition means 42 that allow the user to manually supply metered doses to the central unit 110.

[0075] Management support system 100 further includes a database 150. As discussed below, this database 150 can be used to store models, specifically models 61, 62, 63.

[0076] FIG. 4 shows an example of a management support system 200 located on land and communicating with the ship 1. The ship 1 comprises a central unit 210, a human-machine interface 140 and a communication interface 230. The central unit 1, the communication interface 130 and the database 150 of their parts are located at the ground station 1000. The operation of the management support system 200 is similar to that of the management support system 100, in that it communicates the results of calculations performed by the central unit 1 on the ground to the human-machine interface on board the ship 1 using communication interfaces 130 and 230. The only difference is that it sends to 140. As an example, communication interfaces 130 and 230 may use terrestrial or satellite radio frequency data transmission.

[0077] Here, the management support method 300 for the ship 1 will be described with reference to FIGS. 5A to 9. This method can be implemented using the management support system 100 or 200.

[0078] The method 300 includes a first step 301, which includes providing an initial condition of the tank 3. This initial state includes the initial pressure of the gas phase 3G contained within the tank 3 and the initial temperature of the liquid phase 3L contained within the tank 3. This information is input by the user using the acquisition means 42, for example.

[0079] Preferably, the initial state further includes an initial composition of the liquefied gas contained within the tank 3. This initial composition is usually indicated by the liquefied gas supplier when the liquefied gas is introduced into the tank 3. This initial composition can be input by the user using the acquisition means 42, for example.

[0080] The method 300 further includes step 302, which includes providing a predicted path 80 for the vessel 1. FIG. 6 is a diagram showing an example of the predicted route 80 for explanation. As shown in this figure, the predicted route 80 is defined by a departure point 81, a destination point 82, and a plurality of waypoints 83 through which the ship 1 is assumed to pass. The points 81, 82, and 83 are input by the user using the acquisition means 42, for example. The points 81, 82, 83 are defined by their geographical coordinates in a manner known per se.

[0081] Each pair of consecutive waypoints 83 together define a path step 84. The predicted route 80 may also be defined by the direction of travel of the vessel 1 along the route steps 84.

[0082] The method 300 further includes a step 303 comprising determining at least one environmental parameter of the vessel 1 along the predicted path 80 based on the weather forecast. Specifically, central unit 110 uses communication interface 130 to obtain a weather forecast provided by a weather forecast provider and determines at least one environmental parameter based on the weather forecast and predicted path 80. The environmental parameters include at least one of current direction, current velocity, wind speed, wind direction, mean wave height, wave direction, and wave period, and preferably include some or even all of these parameters. Note that some of these environmental parameters may be provided directly by the weather forecast provider, while other parameters may be estimated by the central unit 10 by applying certain models to the weather forecast.

[0083] The method 300 further includes a step 304 comprising determining a speed curve of the vessel 1 along the predicted path 80. In a simplified embodiment, the velocity curve can simply be provided as input data, for example by the user using the acquisition means 41.

[0084] The method 300 further includes step 305, which includes generating a plurality of tank management scenarios 50.

[0085] FIG. 7 is a graph illustrating an example tank management scenario 50. As shown in this figure, a given tank management scenario 50 defines the change in the pressure P of the gas phase 3G contained within the tank 3 as a function of time t during the course of the predicted path. In a preferred embodiment, the predicted path 80 is divided into successive time intervals d1, d2, d3, d4, d5, d6, etc., and the tank management scenario 50 spans the time intervals d1, d2, d3, d4, d5, d6, etc. Define the average value of pressure P, P1, P2, P3, P4, P5, P6, etc. The time intervals d1, d2, d3, d4, d5, d6, etc. may each specifically be equal to one day.

[0086] Tank management scenario 50 can be randomly generated in step 305, for example.

[0087] After step 305, method 300 proceeds to evaluate tank management scenario 50 (see A in FIGS. 5A and 5B).

[0088] According to a preferred embodiment, detailed further below, the method 300 implements an evolutionary algorithm that uses as an objective function a cost function that depends at least on the total amount of BOG generated in the tank 3 along the predicted path 80. do. Within the context of this evolutionary algorithm, and in a manner known per se, the tank management scenario 50 is iteratively regenerated multiple times, each new generation of the tank management scenario 50 being a combination of the previous generation that best minimizes the cost function. Generated based on a certain number of 50 tank management scenarios.

[0089] FIG. 8 is a block diagram illustrating models 61, 62, 63 implemented to evaluate the cost function to facilitate understanding of the following description. These models 61, 62, 63 can be stored in the database 150 of the management support system 100.

[0090] The first model 61, hereinafter referred to as "engine model 61", estimates the power required to be output from the engine 40, more specifically, the mechanical power required for the output shaft 40A of the engine 40. (See Figure 2). This mechanical power may be the only power necessary for propulsion of the vessel 1. Alternatively, this mechanical power can be combined with the power required to propel the vessel 1 and the power required to electrically power other equipment on the vessel 1 (commonly referred to as "hotel loads"). It can be the sum of In either case, the engine model 61 determines the power required to be output from the engine 40 as a function of the speed set point of the vessel 1, at least one environmental parameter, and optionally the heading of the vessel 1. It can be estimated.

[0091] According to certain embodiments, engine model 61 is a statistical model trained by supervised machine learning techniques. This statistical model is trained on a set of test data, which may be obtained from vessel navigation and navigation tests including measurements of the following items: - on the one hand, the speed of the ship, and at least one environment selected from wave height, wave period, angle between wave direction and the direction of travel of the ship, wind speed, angle between wind direction and the direction of travel of the ship; measure parameters, -On the other hand, it measures the power output from the ship's engines, which is required to maintain the ship's speed.

[0092] The second model 62, hereinafter referred to as "tank model 62", is capable of estimating the amount of BOG generated within the tank 3. Tank Model 62 estimates the amount of BOG generated in a tank by separately estimating the following items: - determining the first amount of BOG generated in the tank 3 as a function of the current pressure P of the gas phase 3G contained in the tank 3 and the current temperature T of the liquid phase 3L contained in the tank 3, and - determining a second BOG quantity generated in the tank 3 as a function of the current pressure P of the gas phase 3G and at least one environmental parameter and then estimating it by adding these two BOG quantities;

[0093] The first BOG amount reflects the occurrence of BOG associated with the coexistence of liquid phase 3L and gas phase 3G. The first BOG amount can be estimated using thermodynamic models known per se, for example the Hertz-Knudsen model.

[0094] The second BOG amount reflects the further generation of BOG in the tank 3 due to the agitation of the liquid phase 3L by the movements experienced by the vessel 1.

[0095] According to a particular embodiment, the second BOG quantity is estimated by a statistical model trained by a supervised machine learning technique. This statistical model is trained on a set of test data, which may be obtained from vessel navigation and navigation tests including measurements of the following items: -On the other hand, it measures the pressure of the gas phase contained in the tank and the temperature of the liquid phase contained in the tank, -On the other hand, it measures the difference between the amount of BOG actually generated in the ships' tanks and the estimated amount of BOG estimated by the thermodynamic model.

[0096] Additionally, as mentioned above, if provided with the initial composition of the liquefied gas contained within tank 3 in step 301, tank model 62 may also estimate the composition of the BOG generated within tank 3. . In this case, the model for estimating the first amount of BOG and the model for estimating the second amount of BOG each estimate the composition of BOG generated in the tank 3.

[0097] A third model 63, hereinafter referred to as "flow model 63", is capable of estimating the amount of gas that should be removed from tank 3 by gas phase processing subsystem 13 and the amount of BOG that should be combusted within GCU 30. More specifically, the flow model 63 estimates the amount of BOG to be supplied to the engine 40 based on the power estimated by the model 61, and calculates the amount of BOG to be supplied to the engine 40 and the amount estimated by the tank model 62. The amount of BOG generated in tank 3 is compared with the amount of BOG generated in tank 3. If the amount of BOG to be supplied to engine 40 is less than the amount of BOG generated within tank 3, flow model 63 determines that the difference between these two amounts should be burned within 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 indicates that the difference between these two amounts should be extracted from tank 3 by gas phase processing subsystem 13. It is determined that

[0098] Models 61, 62, 63 calculate, for each iteration, the pressure P in the gas phase 3G, the temperature T in the liquid phase 3L, the amount of BOG produced by tank 3 (and, if appropriate, the composition of the BOG produced by tank 3). , and is performed repeatedly at very short time intervals for durations such as intervals d1, d2, to estimate the amount of BOG to be supplied to the engine 40 and the GCU 30.

[0099] With further reference to FIG. 5B, the steps for successfully evaluating the cost function will now be described. N is the number of tank management scenarios 50 generated in step 305, and the method 300 performs N evaluations S1, S2, ..., SN for each of the tank management scenarios 50. Although the diagram in FIG. 5B represents evaluations S1, S2, ..., SN performed in succession, they can optionally be performed simultaneously. Each evaluation S1, S2, ..., SN includes steps 401, 402, 403, 404, which will be described later.

[0100] In step 401, the power required to be output from the engine 40 along the predicted path 80 is determined based on the speed curve of the vessel 1 determined in step 304 and the at least one environmental parameter determined in step 303. The curve is estimated using engine model 61.

[0101] At step 402, a curve of the amount of BOG generated within the tank 3 along the predicted path 80 is estimated using the tank model 62.

[0102] In step 403, based on the curve of the power required to be output from the engine 40 estimated in step 401 and the curve of the amount of BOG generated in the tank 3 estimated in step 402, the amount of BOG to be extracted from the tank 3 is determined. A curve for the amount of gas and a curve for the amount of BOG to be combusted within the GCU 30 are estimated using the flow model 63.

[0103] After steps 401 to 403, a cost function is calculated in step 404. More specifically, the cost function is a real number calculated based on the estimates performed in steps 401-403 and quantifying the acceptability of the associated tank management scenario 50.

[0104] In a simple embodiment, the cost function depends only on the total amount of BOG generated in the tank 3 along the predicted path 80. However, it is preferred that the cost function also depends on other quantities or criteria.

[0105] In a preferred embodiment, the cost function is - the difference between the pressure P of the gas phase contained in the tank 3 at the end of the predicted path 80 and the discharge pressure required by the discharge terminal located at the destination point 82; and / or - the difference between the temperature T of the liquid phase contained in the tank 3 at the end of the predicted route 80 and the discharge temperature required by the discharge terminal located at the destination point 82; - Preferably further depends on both of these two differences. In this way, the tank management scenario 50 ensures that the vessel can reach the discharge terminal located at the destination point 82 if the discharge terminal is at discharge pressures and discharge temperatures that approximate those required by the discharge terminal. It is thought that it will become more acceptable.

[0106] Additionally or alternatively, the cost function may also depend on compliance with some or all of the following criteria according to tank management scenario 50: - the pressure P of the gas phase contained within the tank 3 remains below the maximum safe pressure, which is a predefined set point or even encountered by the vessel 1 along the predicted path 80; It may be a variable value that depends on environmental conditions and may be lower, for example in the case of storms or other potentially dangerous navigation conditions; - the pressure P of the gas phase contained in the tank 3 remains above the minimum pressure, which is determined by a predefined setpoint or even as estimated by the flow model 63 of the engine 40; It may be a variable value that depends on the required BOG amount, - The BOG flow rate through the gas phase treatment system 10 remains below the threshold value.

[0107] As mentioned above, each of steps 401-404 is performed within the context of each evaluation S1, S2, ..., SN of tank management scenario 50.

[0108] After performing the evaluations S1, S2, ..., SN, the method 300 proceeds to step 306, which includes checking whether the stopping criteria are verified. In one embodiment, the stopping criterion is a computation time criterion. In other words, the stopping criterion is considered to be verified if the predefined computation time is reached or exceeded. Alternatively, the stopping criterion can be based on the number of iterations.

[0109] In either case, if the stopping criterion is not verified (see "N" in step 306 of Figure 5B), the method 300 performs a Proceeding to step 307, which includes selecting k tank management scenarios 50 from among the tank management scenarios 50, with k<N. The k tank management scenarios 50 selected in step 307 are those that best minimize the cost function calculated in step 404. In an alternative embodiment, k is a preset number. In another alternative embodiment, k is not preset and the k tank management scenarios 50 selected are those whose cost function calculated in step 404 is below a threshold.

[0110] After step 307, method 300 proceeds to step 308, which includes generating N new tank management scenarios 50 based on the k tank management scenarios 50 selected in step 307.

[0111] As mentioned above, in this embodiment, method 300 uses the cost function calculated in step 404 as an objective function to implement an evolutionary algorithm. In certain embodiments, the evolutionary algorithm is a genetic algorithm. Optimization techniques using evolutionary algorithms are known per se. Step 305 then includes initializing the group considered by the evolutionary algorithm by randomly generating N tank management scenarios 50, and step 308 includes the tank management scenarios selected in step 307. It involves generating N new 50 tank management scenarios by multiplying and transforming into new forms.

[0112] Conversely, if the stopping criterion is verified (see "O" in step 306 of Figure 5B), the method 300 uses the N tanks evaluated during the evaluation S1, S2, ..., SN run. Proceeding to step 309, which includes selecting p tank management scenarios 50 from among the management scenarios 50, with 1≦p<N. The tank management scenario 50 selected in step 307 is the one that best minimizes the cost function calculated in step 404. In an alternative embodiment, p is a preset number. In another alternative embodiment, p is not preset and the selected p tank management scenarios are those whose cost function calculated in step 404 is below a threshold.

[0113] After step 309, the method 300 proceeds to step 310, which comprises displaying the tank management scenario selected in step 309 to the user, for example on display means 41.

[0114] In a highly simplified alternative embodiment, a single tank management scenario 50 is generated in step 305. Steps 401-404 are then performed for this single tank management scenario 50, and steps 306, 307, and 308 are omitted. Step 309 includes determining whether tank management scenario 50 is acceptable as a function of the cost function calculated in step 404. If acceptable, this tank management scenario 50 is displayed in step 310.

[0115] In another simplified alternative embodiment, multiple tank management scenarios 50 are generated in step 308, but steps 306, 307, and 308 are omitted.

[0116] The steps of method 300 may be repeated at set intervals and / or upon receipt of new weather forecasts by communication interface 130. Additionally or alternatively, the steps of method 300 can be repeated in response to instructions by a user.

[0117] In any case, method 300 will display at step 310 at least one tank management scenario 50 to the user, for example on display means 41, as a function of the cost function calculated at step 404. Accordingly, method 300 allows decision-making assistance to be provided to a user.

[0118] As mentioned above, a given tank management scenario 50 consists of an average value of pressure P over successive time intervals d1, d2, d3, d4, d5, d6, etc. P1, P2, P3, P4, P5, P6, etc. can be defined. In a preferred embodiment, after step 309 and before step 310, the one or more tank management scenarios 50 selected in that step span successive time intervals d1, d2, d3, d4, d5, d6, etc. The minimum and maximum values ​​of pressure P can also be post-processed to be displayed to the user.

[0119] FIG. 9 is a graph illustrating an example of a tank management scenario 50 that has been post-processed in this manner. As shown in this figure, the tank management scenario 50 defines, in addition to the average value P1, a maximum value M1 and a minimum value m1 over an interval d1, and the pressure P can be greater than the maximum value M1 or the minimum value Nor can it be made smaller than m1. Similarly, over each interval d1, d2, d3, d4, d5, d6, etc., the tank management scenario 50 has maximum values ​​M2, M3, M4, M5, M6, etc., and minimum values ​​m2, m3, m4, m5, m6 etc. are defined respectively.

[0120] The maximum value M1 is calculated as a function of the average value P1 and as a function of one or more constraints on the tank 3. Constraints for tank 3 may include safety constraints for tank 3, such as a maximum safe pressure for the tank. All or part of the constraints regarding the tank 3 can be input by the user using the acquisition means 42.

[0121] The maximum values ​​M2 to M6 are similarly calculated as a function of the average values ​​P2 to P6 and one or more constraints regarding the tank 3.

[0122] The minimum value m1 is calculated using the flow model 63 as a function of the average value P1 and the amount of gas to be removed from the tank 3 during the interval d1 estimated in step 403.

[0123] The minimum values ​​m2 to m6 are similarly calculated as functions of the average values ​​P2 to P6 and the amount of gas to be removed from the tank 3 during the interval d2 to d6.

[0124] In a simple alternative embodiment, one or more tank management scenarios 50 post-processed in this way can be simply displayed to the user, i.e. the minimum values ​​m1~m6 and the corresponding maximum values ​​M1~M6 can be displayed together. This provides further insight by further displaying to the user the range of values, i.e. the intervals [m1, M1], [m2, m2], etc., in which the pressure P can be varied within the range of the intervals d1, d2, etc. Decision support can be provided to the user.

[0125] As an alternative embodiment, only the maximum values ​​M1-M6 or only the minimum values ​​m1-m6 can be calculated and displayed.

[0126] In a particularly preferred alternative embodiment, the one or more tank management scenarios 50 thus post-processed also determine, before step 310, the optimal value of the pressure P over sub-intervals of the interval d1, d2, etc. You can proceed to further optimization steps aimed at

[0127] FIG. 10 is a graph illustrating an example of such an optimized tank management scenario 50A. As shown in this figure, the time interval d1 is subdivided into several consecutive sub-time intervals i1, i2, i3, i4, i5, i6, etc. The time intervals i2, i2, i3, i4, i5, i6, etc. may specifically each be equal to one hour. Tank management scenario 50A has average values ​​Pi1, Pi2, Pi3, Pi4, Pi5, Define Pi6. Similarly, although this point is not shown in Fig. 10 for brevity, the time intervals d2, d3, d4, d5, d6, etc. are also subdivided into several consecutive sub-time intervals and the pressure A mean value of P is defined for each of these subintervals.

[0128] The average values ​​Pi1, Pi2, etc. are determined by an evolutionary algorithm, using as objective function a cost function that depends at least on the total amount of BOG generated in tank 3 during time interval i1 and on compliance with the following criteria: -The pressure P of the gas phase contained in tank 3 remains below the maximum value M1, - the pressure P of the gas phase contained in tank 3 remains above the minimum value m1; - the flow rate of BOG through the gas phase treatment system 10 remains below a threshold value; Determining this average value is performed using steps similar to steps 401-404-306-308, so the details will not be repeated. One or more tank management scenarios 50A are then displayed to the user.

[0129] As mentioned above, the speed curve can be simply provided as input data, for example by the user using the acquisition means 41 in step 304. However, in step 304 the velocity curve is calculated between the total amount of BOG generated in tank 3 along the predicted path 80 and the estimated time of arrival at the destination of vessel 1 and the estimated time of arrival at the destination of vessel 1. Preferably, it is determined by an evolutionary algorithm, using an objective function that depends on the difference between . The time required for the ship 1 to arrive at the destination can be input by the user using the acquisition means 42.

[0130] Within the context of this evolutionary algorithm, and in a manner known per se, the velocity management scenario 550 is iteratively generated multiple times, each new generation of the velocity management scenario 550 being a number of previous generations that best minimizes the objective function. Generated from the speed management scenario 550.

[0131] FIG. 11 is a graph illustrating an example speed management scenario 550. As shown in this figure, if the predicted route 80 is divided into consecutive legs of travel distance such as y1, y2, y3, y4, y5, then the given speed management scenario 550 Changes in the speed V of the vessel 1 are defined by defining average values ​​V1, V2, V3, V4, V5, etc. of the speed V over each continuous section of the moving distance such as y4, y5. The travel distance intervals y1, y2, y3, y4, y5, etc. can advantageously be defined with reference to points 81, 82, 83 of the predicted route 80.

[0132] With reference to FIG. 12, the steps for implementing the evolutionary algorithm will now be described.

[0133] During step 601, for example, multiple speed management scenarios 550 are randomly generated.

[0134] M is the number of rate management scenarios 550 generated in step 601, and after step 601, M evaluations R1, R2, ..., RM are performed for each rate management scenario 550. Although the diagram of FIG. 12 shows the evaluations R1, R2, ..., RM to be performed sequentially, they can optionally be performed simultaneously. Each evaluation R1, R2,..., RM includes steps 602, 603, 604, 605, 606 described below.

[0135] At step 602, the effective speed curve of the vessel 1 is estimated using the hydrodynamic model of the vessel 1.

[0136] At step 603, based on the effective speed curve estimated at step 602, the effective trajectory of the vessel 1 is estimated using an ocean current model.

[0137] At step 604, at least one environmental parameter of the vessel 1 is determined along the effective trajectory of the vessel 1 based on the weather forecast. Specifically, the central unit 110 uses the communication interface 130 as in step 303 to obtain the weather forecast provided by the weather forecast provider and uses the weather forecast and the estimated effective At least one environmental parameter is determined based on the trajectory.

[0138] In step 605, a curve of the amount of BOG generated in the tank 3 along the effective trajectory of the vessel 1 is estimated using the tank model 62.

[0139] After steps 601 to 605, an objective function is calculated in step 606. The objective function is a real number that quantifies the acceptability of the speed management scenario 550. As mentioned above, the objective function is based on the total amount of BOG generated in tank 3 along the predicted path 80, the time required to arrive at the destination of vessel 1, and the estimated time of arrival at the destination of vessel 1. It depends on the difference between.

[0140] After performing the evaluations R1, R2, ..., RN, the method proceeds to step 607, which includes checking whether the stopping criteria are verified. In one embodiment, the stopping criterion is a computation time criterion. In other words, the stopping criterion is considered to be verified if the predefined computation time is reached or exceeded. Alternatively, the stopping criterion can be based on the number of iterations.

[0141] In either case, if the stopping criterion is not verified (see "N" in step 607 of Figure 12), the method evaluates R1, R2, ..., M evaluated during the RM run. Proceeding to step 608, which includes selecting k′ speed management scenarios 550 from among the speed management scenarios 550, with k′<M. The k′ speed management scenarios 550 selected in step 608 are those that best minimize the objective function calculated in step 606. In an alternative embodiment, k' is a preset number. In another alternative embodiment, k is not preset and the k' speed management scenarios 550 selected are those whose objective function calculated in step 608 is below a threshold.

[0142] After step 608, the method proceeds to step 609, which includes generating M new tank management scenarios based on the k' speed management scenarios 550 selected in step 608.

[0143] In certain embodiments, the evolutionary algorithm is a genetic algorithm. Step 601 then includes initializing the group considered by the evolutionary algorithm by randomly generating M rate management scenarios 550, and step 609 includes the rate management scenarios selected in step 608. 550 to generate M new speed management scenarios 550 by multiplying them into new forms.

[0144] Conversely, if the stopping criterion is verified (see "O" in step 607 of FIG. 12), the method selects the speed management scenario 550 that best minimizes the objective function calculated in step 606. Proceed to step 610, which includes: Finally, in step 611, this speed management scenario 550 is specified as the speed curve of vessel 1 along the predicted path in question, and then steps 305-310 and evaluations S1, S2, ..., SN are already It can be implemented as described above.

[0145] The configuration of the gas phase treatment system 10 shown in FIG. 2 is only an example. Other configurations are also possible.

[0146] In some alternative embodiments, not shown, the vessel 1 may be equipped with two or more engines 40. In this case, the engine model 61 estimates the power required to be output from the engine 40 as a function of the speed set point of the vessel 1, the at least one environmental parameter, and optionally the heading of the vessel 1. be able to.

[0147] As discussed above with respect to FIG. 2, the engine 40 provides the power necessary for propulsion of the vessel 1 and for electrically powering other equipment on the vessel 1 (commonly referred to as "hotel loads"). It is possible to supply both the power and the power required for. However, as shown in Figure 13, the vessel 1 may be equipped with an auxiliary engine 49 intended to supply this "hotel load" power. Auxiliary engine 49, like engine 40, can consume BOG generated from BOG distribution unit 20 or BOG generated from heater 60. In this case, engine model 61 is required to output power from engine 40 and auxiliary engine 49 as a function of the speed set point of vessel 1, at least one environmental parameter, and optionally the heading of vessel 1. Power can be estimated. It should also be specified that, as an alternative embodiment, several auxiliary engines 49 can be provided, in which case the engine model 61 would likewise be required to output power from the engine 40 and the auxiliary engine 49. Power can be estimated.

[0148] FIG. 14 shows another alternative embodiment of a gas phase treatment system 10 further comprising a reliquefaction plant 75.

[0149] In a manner known per se, the reliquefaction plant 75 can receive the BOG originating from the BOG distribution unit 20, reliquefy this BOG and then return this BOG to the tanks 3, 4, 5, 6. Since such reliquefaction plants 75 are known per se, no further details are given here.

[0150] Although not shown in FIG. 14, it should be specified that the power (eg, electrical power) necessary to operate the reliquefaction plant 75 may be provided by the engine 40 or even by the auxiliary engine 49. It should also be specified that in some alternative embodiments not shown, the reliquefaction plant 75 can only deliver reliquefied BOG to one or some of the tanks 3, 4, 5, 6.

[0151] Of course, in this alternative embodiment, the flow model 63 takes into account the presence of a reliquefaction plant 75.

[0152] More specifically, the flow model 63 estimates the amount of gas that should be removed from the tank 3 by the gas phase processing subsystem 13, the amount of BOG that should be combusted within the GCU 30, and the amount of BOG that should be delivered to the reliquefaction plant 75. can do. More specifically, the flow model 63 estimates the amount of BOG to be supplied to the engine 40 based on the power estimated by the model 61, and calculates the amount of BOG to be supplied to the engine 40 and the amount estimated by the tank model 62. The amount of BOG generated in tank 3 is compared with the amount of BOG generated in tank 3. 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 indicates that the difference between these two amounts should be removed from tank 3 by gas phase processing subsystem 13. judge. Conversely, if the amount of BOG to be fed to the engine 40 is less than the amount of BOG generated in the tank 3, the flow model 63 should preferably deliver the difference between these two amounts to the reliquefaction plant 75. , it is determined that the difference amount should be combusted within the GCU 30 only when the reliquefaction capacity of the reliquefaction plant 75 is insufficient.

[0153] In addition, in this alternative embodiment, in step 403, the curve of the power required to be output from the engine 40 estimated in step 401 and the curve of the amount of BOG generated in the tank 3 estimated in step 402 are determined. Based on this, a curve for the amount of gas to be removed from the tank 3, a curve for the amount of BOG to be burned in the GCU 30, and a curve for the amount of BOG to be delivered to the reliquefaction plant 75 are estimated using the flow model 63. It is also understood that

[0154] FIG. 15 shows yet another alternative embodiment of the gas phase treatment system 10 further comprising a subcooler 77.

[0155] In a manner known per se, the subcooler 77 can receive a portion of the liquid phase 3L contained in the tank 3 from the gas phase treatment subsystem 13 and reduce its temperature (for example, when the liquefied gas is lowered to atmospheric pressure). of LNG, by about 5°C to about 10°C, i.e. from about -162°C to about 167°C to -172°C). The subcooler 77 also directs the liquid phase supercooled in this way into the liquid phase 3L using a mixing nozzle 79, which is known per se, or into droplets using a jetting lamp 78, which is known per se. It can be indirectly returned to the tank 3 by spraying it into the gas phase 3G.

[0156] Although not shown in FIG. 15, it should be specified that the power (eg, electric power) necessary for operation of subcooler 77 may be provided by engine 40 or even by auxiliary engine 49. In some alternative embodiments, not shown, the subcooler 77 removes the liquid phase from some or possibly all tanks 3, 4, 5, 6 and disposes this liquid phase in a subcooled state, respectively. It should also be specified that it can be returned to the tank.

[0157] Of course, in this alternative embodiment, flow model 63 takes into account the presence of subcooler 77.

[0158] More specifically, flow model 63 estimates the amount of gas that should be removed from tank 3 by gas phase processing subsystem 13, the amount of BOG that should be combusted within GCU 30, and even the amount of liquid phase that should be delivered to subcooler 77. be able to. More specifically, the flow model 63 estimates the amount of BOG to be supplied to the engine 40 based on the power estimated by the model 61, and calculates the amount of BOG to be supplied to the engine 40 and the amount estimated by the tank model 62. The amount of BOG generated in tank 3 is compared with the amount of BOG generated in tank 3. 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 indicates that the difference between these two amounts should be removed from tank 3 by gas phase processing subsystem 13. judge. Conversely, if the amount of BOG to be supplied to the engine 40 is less than the amount of BOG generated in the tank 3, the flow model 63 calculates the difference between these two amounts over time, preferably by operating the subcooler 77. Only when the supercooling capacity of the subcooler 77 is insufficient, and if there is surplus BOG, it is determined that it should be combusted within the GCU 30.

[0159] In addition, in this alternative embodiment, in step 403, the curve of the power required to be output from the engine 40 estimated in step 401 and the curve of the amount of BOG generated in the tank 3 estimated in step 402 are determined. Based on this, the curve for the amount of gas to be taken out from tank 3, the curve for the amount of BOG to be burned in GCU 30, and the curve for the amount of liquid phase to be taken out from tank 3 and delivered to subcooler 77 are calculated using flow model 63. It is also clearly understood that it is assumed that

[0160] Finally, in some alternative embodiments not shown, the subcooler 77 removes the liquid phase from one of the tanks 3, 4, 5, 6 in a supercooled state. It should also be specified that this liquid phase can be returned to one or more of the 6. In this case, as mentioned above, when the management support method 300 is performed simultaneously on some or each of the tanks 3, 4, 5, 6, the flow model 63 for a given tank is 1 Further estimating the amount of supercooled liquid phase to be received from subcooler 77 originating from one or more other tanks.

[0161] Although the invention has been described with reference to some particular embodiments, it is not intended that the invention be limited thereto in any way and that all described means may be used while these are within the scope of the invention. , and combinations thereof.

[0162] Use of the verbs "comprise" or "include" and their conjugations does not exclude the presence of elements or steps other than those stated in a claim.

[0163] In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim.

Claims

1. A computer implementation method for assisting the management of a liquefied gas transport vessel (1), wherein the vessel comprises at least one tank (3) configured to contain liquefied gas, and a gas phase processing system (10) which can deliver boil-off gas released from the tank (3) to the vessel's propulsion engine (40) or a gas combustion unit (30) mounted on the vessel, and the gas phase processing system (10) can further extract a portion of the liquid phase (3L) contained in the tank (3), vaporize the portion, and deliver it to the propulsion engine (40), and the method comprises, - Step (301) of providing an initial state of the tank, wherein the initial state of the tank includes 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), - The step (302) of providing a predicted path (80) for the vessel (1), - A step (303) of determining at least one environmental parameter of the vessel along the predicted route (80) based on the weather forecast, - A step (304) to determine the speed curve of the vessel (1) along the predicted path (80), - A step (305) of generating a tank management scenario (50, 50A) that defines the change in pressure of the gas phase (3G) contained in the tank while the predicted path (80) is in progress, a) Step (401) Estimate, based on the tank management scenario, a power curve that needs to be output from the propulsion engine (40) along the predicted path (80) as a function of the speed curve of the vessel and the at least one environmental parameter. b) Step (402) Estimate a curve of the amount of boil-off gas generated in the tank (3) along the predicted path, based on the tank management scenario using a tank model capable of estimating the amount of boil-off gas generated in the tank; c) A step (403) to estimate, based on the required power curve and the boil-off gas generation curve, the boil-off gas generation curve to be taken out of the tank and the boil-off gas generation curve to be burned in the gas combustion unit (30), and d) A step (305) to generate a tank management scenario, which involves a step (404) to calculate a cost function that depends at least on the total amount of boil-off gas generated in the tank along the predicted path (80), - A step (310) of displaying the tank management scenario (50, 50A) to the user as a function of the calculated cost function, A computer implementation method for supporting the management of a liquefied gas transport vessel (1), including the following.

2. The method according to claim 1, wherein multiple tank management scenarios (50, 50A) are generated, steps a) to d) are performed for each tank management scenario, and at least one of the tank management scenarios is displayed to the user as a function of the calculated cost function for the tank management scenario.

3. The method according to claim 2, wherein, prior to the step (310) of displaying at least one of the tank management scenarios (50, 50A) to the user, the tank management scenarios are iteratively regenerated many times by a first evolutionary algorithm using 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 a path step (84) defined by two waypoints (83) and the direction of travel between the two waypoints, and in step a), the curve of the power that needs to be output from the propulsion engine (40) along the predicted path (80) is estimated as a function of the direction of travel, the speed curve of the vessel 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 vessel is determined from the predicted path (80) and the at least one environmental parameter by a second evolutionary algorithm that uses a second objective function which depends on the total amount of boil-off gas generated in the tank along the predicted path (80) and the difference between the time required to arrive at the destination and the estimated time of arrival at the destination.

6. The method according to any one of claims 1 to 5, wherein the step (302) of providing a predicted route (80) of the vessel includes the step of providing an unloading pressure and / or unloading temperature required by the unloading terminal at the destination of the vessel, and in step d), the cost function further depends on the difference between the pressure of the gas phase (3G) contained in the tank (3) at the end of the predicted route (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 route (80) and the unloading temperature.

7. The method according to any one of claims 1 to 6, wherein in step a), the required power curve is estimated using a first statistical model, the first statistical model is capable of estimating the power that needs to be output from the propulsion engine (40) as a function of at least a ship speed setting point and the at least one environmental parameter, and 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 step b) includes the step (402) of estimating a curve of the amount of boil-off gas generated in the tank (3) along the predicted path (80), and step b) includes the steps of: estimating a first amount of boil-off gas generated in the tank as a function of the current pressure of the gas 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 boil-off gas generated in the tank as a function of the current pressure of the gas phase (3G) contained in the tank and the at least one environmental parameter.

9. The method according to claim 8, wherein the amount of the second boil-off gas generated in the tank (3) is estimated using a second statistical model, and the second statistical model is 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) further includes the step of estimating the composition of the boil-off gas generated in the tank (3) along the predicted path (80), the step (402) comprising estimating the composition of the boil-off gas generated in the tank.

11. The method according to any one of claims 1 to 10, wherein the environmental parameter includes at least one of flow direction, flow velocity, 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 gas phase treatment system (10) further comprises a reliquefaction plant (75) capable of reliquefying the boil-off gas released from the tank (3) and returning the reliquefied boil-off gas to the tank, and step c) includes a step (403) of estimating, based on the required power curve and the boil-off gas generation curve, a curve of the amount of gas to be taken out of the tank, a curve of the amount of boil-off gas to be burned in the gas combustion unit (30), and a curve of the amount of boil-off gas to be delivered to the reliquefaction plant (75).

13. The method according to any one of claims 1 to 12, wherein the gas phase treatment system (10) further comprises a subcooler (77) that can supercool a portion of the liquid phase (3L) contained in the vessel (3) and return the thus supercooled portion to the tank, and step c) includes a step (403) of estimating a curve of the amount of gas to be taken out of the tank, a curve of the amount of boil-off gas to be burned in the gas combustion unit (30), and a curve of the amount of liquid phase to be taken out of the tank and delivered to the subcooler (77), based on the required power curve and the curve of the amount of boil-off gas generated.

14. The method according to any one of claims 1 to 13, wherein the step of the method according to claim 1 is repeated at regular intervals and / or when a new weather forecast is received.

15. A management support system (100, 200) for a liquefied gas transport vessel, wherein the vessel (1) comprises at least one tank (3) configured to contain liquefied gas, and a gas phase treatment system (10) which can deliver boil-off gas released from the tank (3) to the vessel's propulsion engine (40) or a gas combustion unit (30) mounted on the vessel, and the gas phase treatment system (10) can further extract a portion of the liquid phase (3L) contained in the tank (3), vaporize the portion, and deliver it to the propulsion engine (40), and the management support system (100, 200) comprises, - An acquisition unit (42) configured to acquire the initial state of the tank and the predicted path (80) of the vessel, wherein the initial state of the tank includes 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 weather forecasts from a weather forecast provider, - A calculation unit (110) configured to determine the speed curve of the vessel along the predicted path (80) (304), and to generate a tank management scenario (50, 50A) that defines the change in pressure of the gas phase (3G) contained in the tank (3) along the predicted path (80), wherein the calculation unit (110) determines the speed curve of the vessel along the predicted path (80) (304), and to generate a tank management scenario (50, 50A) that defines the change in pressure of the gas phase (3G) contained in the tank (3) along the predicted path (80), wherein the calculation unit (110) determines the tank management scenario thus generated, a) As a function of the speed curve of the vessel and at least one environmental parameter, estimate the power curve that needs to be output from the propulsion engine (40) along the predicted path (80) (401), b) Estimate the curve of the amount of boil-off gas generated in the tank (3) along the predicted path (402), c) Based on the required power curve and the boil-off gas generation curve, estimate the curve of the amount of gas to be taken from the ship and the curve of the amount of boil-off gas to be burned in the gas combustion unit (30) (403), d) A calculation unit (110) configured to calculate a cost function (404) that depends at least on the total amount of boil-off gas generated in the tank along the predicted path (80), - A display unit (41) configured to display the tank management scenario (50, 50A) to the user as a function of the calculated cost function, Equipped with, A management support system (100, 200) for liquefied gas transport vessels, wherein the acquisition unit (42) and the display unit (41) are mounted on the vessel, the first communication unit (130) and the calculation unit (110) are located at a ground station (1000), and the system further comprises a second communication unit (230) configured to connect the acquisition unit (42) and the calculation unit (110).

16. The system according to claim 15 (100, 200), wherein the predicted path (80) includes path steps (84) defined by two waypoints (83) and the direction of travel between the two waypoints, and the computing unit (110) is configured to a) estimate the power curve that will need to be output from the propulsion engine (40) along the predicted path (80) as a function of the direction of travel, the speed curve of the vessel and at least one environmental parameter (401).

Images