WO2020195120A1 - Appareil, procédé et programme de traitement - Google Patents

Appareil, procédé et programme de traitement Download PDF

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Publication number
WO2020195120A1
WO2020195120A1 PCT/JP2020/003013 JP2020003013W WO2020195120A1 WO 2020195120 A1 WO2020195120 A1 WO 2020195120A1 JP 2020003013 W JP2020003013 W JP 2020003013W WO 2020195120 A1 WO2020195120 A1 WO 2020195120A1
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WIPO (PCT)
Prior art keywords
tank
raw material
liquefied raw
receiving operation
processing apparatus
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PCT/JP2020/003013
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English (en)
Inventor
Toshiya Okazaki
Yoshihisa Hidaka
Junichi Watanabe
Tsuyoshi Shimada
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Yokogawa Electric Corporation
Yokogawa Solution Service Corporation
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Priority to JP2021533683A priority Critical patent/JP2022525381A/ja
Publication of WO2020195120A1 publication Critical patent/WO2020195120A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
    • G06Q10/06375—Prediction of business process outcome or impact based on a proposed change
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply

Definitions

  • the present invention relates to a processing apparatus, a processing method, and a processing program.
  • Patent Document 1 Japanese Patent Application Publication No. 2013-164672
  • the processing apparatus may comprise a forecasting model that receives, as input, a tank state of a tank that stores a liquefied raw material and a receiving operation scheduling of the liquefied raw material, and forecasts the tank state in the future based on at least a change of a density of the liquefied raw material in the tank.
  • the processing apparatus may comprise an operation plan output section that outputs an operation plan of a plant including the tank, based on the forecasted tank state.
  • the processing apparatus may further comprise a receiving operation scheduling updating section that updates the receiving operation scheduling, based on the forecasted tank state.
  • the processing apparatus may further comprise a flow rate calculating section that calculates a flow rate of an additive raw material that is added to the liquefied raw material, based on the forecasted tank state.
  • the receiving operation scheduling updating section may update the receiving operation scheduling using an objective function that includes at least the flow rate of the additive raw material in a component thereof.
  • the receiving operation scheduling updating section may update the receiving operation scheduling in a manner to minimize the flow rate of the additive raw material.
  • the receiving operation scheduling updating section may update the receiving operation scheduling in a manner to minimize work relating to the tank.
  • the receiving operation scheduling may include a plan concerning at least one of which of a plurality of tanks is to receive the liquefied raw material, which of the plurality of tanks the liquefied raw material is to be discharged from, and which of the plurality of tanks the liquefied raw material is to be transferred between.
  • the forecasting model may forecast the tank state in the future based on at least a change of a BOG obtained by vaporizing the liquefied raw material.
  • the forecasting model may forecast the tank state in the future based on at least a density of the liquefied raw material stored in the tank and a density of the liquefied raw material to be received in the tank.
  • the processing apparatus may further comprise a discharge state acquiring section that acquires a discharge state of the liquefied raw material when the liquefied raw material is discharged from the tank; and a model updating section that updates the forecasting model based on the discharge state of the liquefied raw material and the forecasted tank state.
  • the processing method may comprise receiving, as input, a tank state of a tank that stores a liquefied raw material and a receiving operation scheduling of the liquefied raw material, and forecasting the tank state in the future based on at least a change of a density of the liquefied raw material in the tank.
  • the processing method may comprise outputting an operation plan of a plant including the tank, based on the forecasted tank state.
  • a processing program may be executed by a computer.
  • the processing program may cause the computer to function as a forecasting model that receives, as input, a tank state of a tank that stores a liquefied raw material and a receiving operation scheduling of the liquefied raw material, and forecasts the tank state in the future based on at least a change of a density of the liquefied raw material in the tank.
  • the processing program may cause the computer to function as an operation plan output section that outputs an operation plan of a plant including the tank, based on the forecasted tank state.
  • FIG. 1 shows an example of a block diagram of a processing apparatus 100 according to the present embodiment.
  • FIG. 2 shows an example of a plant 200 to be controlled by a processing apparatus 100 according to the present embodiment.
  • FIG. 3 shows an example of a process flow of the processing apparatus 100 according to the present embodiment.
  • Fig. 4 shows an example of a block diagram of the processing apparatus 100 according to a modification of the present embodiment.
  • FIG. 5 shows an example of a block diagram of the processing apparatus 100 according to another modification of the present embodiment.
  • FIG. 6 shows an example of a computer 2200 in which aspects of the present invention may be wholly or partly embodied.
  • Fig. 1 shows an example of a block diagram of a processing apparatus 100 according to the present embodiment.
  • the processing apparatus 100 forecasts in detail a future state of a tank that stores a liquefied raw material, and outputs an operation plan for a plant that includes this tank.
  • the processing apparatus 100 may be a computer such as a PC (personal computer), a tablet computer, a smart phone, a work station, a server computer, a general purpose computer, or the like, or may be a computer system in which a plurality of computers are connected. Such a computer system is also a computer, in a broad sense.
  • the processing apparatus 100 may be implemented in a virtual computer environment that can be executed in one or more computers. Instead, the processing apparatus 100 may be a specialized computer designed for the purpose of work at the production site, or may be specialized hardware realized by specialized circuitry. If the processing apparatus 100 is capable of connecting to the Internet, the processing apparatus 100 may be realized by cloud computing.
  • the processing apparatus 100 of the present embodiment may have, as a control target, a plant that handles LNG as the liquefied raw material, for example.
  • the LNG is made from a mixture including methane serving as the main component, components such as ethane, propane, butane, pentane, and nitrogen, and the ratio of each component contained therein differs greatly depending on the production area.
  • the LNG is a substance obtained by cooling a natural gas made from such a mixture to approximately 162°C and liquefying this gas, and since liquefying this gas causes the volume thereof to decrease to approximately 1/600 of the original volume, it is possible to transport a large amount of this liquefied gas in a tanker.
  • the processing apparatus 100 has, as a control target, a plant that handles such LNG as a liquefied raw material, but the present invention is not limited to this.
  • the processing apparatus 100 may have a plant that handles another liquefied raw material as a control target.
  • the processing apparatus 100 includes a receiving operation scheduling acquiring section 110, a tank state acquiring section 120, a forecasting model 130, a receiving operation scheduling updating section 140, and an operation plan output section 150.
  • the receiving operation scheduling acquiring section 110 acquires a liquefied raw material receiving operation scheduling.
  • the receiving operation scheduling acquiring section 110 acquires the receiving operation scheduling from another apparatus or another function section within the same apparatus via a network, for example.
  • the receiving operation scheduling acquiring section 110 may receive a keyboard manipulation, mouse manipulation, and the like performed by a user to acquire the receiving operation scheduling via user input, or may acquire the receiving operation scheduling via a memory device or the like capable of storing data.
  • the receiving operation scheduling acquiring section 110 may acquire the updated receiving operation scheduling from the receiving operation scheduling updating section 140.
  • the receiving operation scheduling acquiring section 110 supplies the forecasting model 130 with the acquired receiving operation scheduling.
  • the tank state acquiring section 120 acquires a tank state that indicates the state of a tank storing the liquefied raw material.
  • the tank state acquiring section 120 acquires the tank state from the plant that is the control target, via a network, for example.
  • the tank state acquiring section 120 may receive a keyboard manipulation, mouse manipulation, and the like performed by a user to acquire the tank state via user input, or may acquire the tank state via a memory device or the like capable of storing data.
  • the tank state acquiring section 120 may acquire the tank state from the DCS.
  • the tank state may be acquired from a data historian apparatus or an LNG calculator.
  • the tank state may be acquired from a PIMS (Plant Information Management System).
  • the tank state acquiring section 120 supplies the forecasting model 130 with the acquired tank state.
  • the forecasting model 130 receives, as input, the tank state of the tank storing the liquefied raw material and the liquefied raw material receiving operation scheduling, and forecasts the future tank state based on at least a change in BOG obtained by vaporizing the liquefied raw material in the tank and a change in a density caused by a method of receiving the liquefied raw material. The details of this process are described further below.
  • the forecasting model 130 supplies the receiving operation scheduling updating section 140 with the forecasted tank state.
  • the receiving operation scheduling updating section 140 updates the receiving operation scheduling, based on the forecasted tank state.
  • the receiving operation scheduling updating section 140 supplies the receiving operation scheduling acquiring section 110 and the operation plan output section 150 with the updated receiving operation scheduling.
  • the operation plan output section 150 outputs an operation plan of a plant that includes the tank storing the liquefied raw material, based on the forecasted tank state.
  • the operation plan output section 150 outputs the operation plan of the plant based on the updated receiving operation scheduling.
  • the operation plan output section 150 then outputs the operation plan to the plant that is the control target, via the network, for example.
  • the operation plan output section 150 may output the operation plan to a monitor, or may write the operation plan to a memory device or the like capable of storing data.
  • the operation plan output section 150 may output the operation plan to the DCS.
  • Fig. 2 shows an example of a plant 200 that is the control target of the processing apparatus 100 according to the present embodiment.
  • the plant 200 may be controlled by a DCS.
  • the DCS may include a control apparatus for each device forming the system, for example, and these control apparatuses may be connected to a network and be capable of communicating with and monitoring each other.
  • the present drawing describes an example in which the plant 200 handles the LNG serving as the liquefied raw material, reverts the LNG from a liquid to a gas using a heat source, and also, after adding LPG (Liquefied Petroleum Gas) and adjusting the gas to be a predetermined supply heat amount, adds an odorant for safety and supplies this gas to customers as municipal gas.
  • LPG Liquefied Petroleum Gas
  • the gas obtained by reverting the LNG from a liquid to a gas using a heat source is supplied as fuel for power generation.
  • a natural gas field, a liquefaction base, maritime transportation by LNG tanker, an LNG reception station, and the like are integrated to form an LNG chain.
  • the plant 200 that is the control target of the processing apparatus 100 according to the present embodiment may be the LNG reception station in such an LNG chain, for example.
  • the plant 200 may be a reception station for receiving an LNG tanker loaded with LNG as the liquefied raw material and an LPG tanker loaded with LPG as an additive raw material, for example.
  • the plant 200 includes a plurality of LNG tanks 210a to 210n (referred to collectively as “LNG tanks 210"), an LPG tank 220, a plurality pieces of vaporization equipment 230a to 230n (referred to collectively as "vaporization equipment 230”), heat adjustment equipment 240, and odor equipment 250.
  • the LNG tank 210 is a storage tank for storing the LNG.
  • the LNG tank 210 is on land, underground, or the like, but the LNG tank 210 may be any type of tank.
  • the LNG tank 210 receives and stores therein the LNG loaded in the LNG tanker, via an unloading arm. The LNG tank 210 then discharges the stored LNG when supplying the municipal gas to the customers.
  • Received LNG is defined as the LNG received in the LNG tank 210
  • stored LNG is defined as the LNG stored in the LNG tank 210
  • discharged LNG is defined as the LNG discharged from the LNG tank 210, respectively.
  • the LNG tanks 210 are tanks that are capable of transferring the LNG among any one of a plurality of tanks.
  • the LPG tank 220 is a storage tank for storing the LPG.
  • the LPG tank 220 receives and stores therein the LPG loaded in the LPG tanker, via the unloading arm.
  • the LPG tank 220 then discharges the stored LPG as the additive raw material, when supplying the municipal gas to the customers.
  • the vaporization equipment 230 is equipment that reverts the discharged LNG that has been discharged from the LNG tank 210 into a gas, when supplying the municipal gas or power generation fuel to the customers.
  • the vaporization equipment 230 may be an open rack type using seawater as the heat source, a submerged type using hot water heated by submerged combustion burner as a heat source, or the like, or may be any type of vaporization equipment 230.
  • an example is described in which a plurality of pieces of vaporization equipment 230 are provided respectively for the outputs of a plurality of LNG tanks 210, but the present invention is not limited to this.
  • Pieces of vaporization equipment 230 may be provided at various locations where it is possible to vaporize LNG, and may be provided at the output of the heat adjustment equipment 240, for example.
  • the vaporization equipment 230 supplies the heat adjustment equipment 240 with natural gas obtained by vaporizing the LNG.
  • the heat adjustment equipment 240 is equipment for adjusting the heat of the natural gas by adding an additive such as LPG or N 2 to the natural gas, such that the heat of the natural gas obtained by vaporizing the LNG becomes a predetermined supply heat amount, e.g. approximately 40 to 45 MJ/m 3 N in a case where this gas is to be supplied as municipal gas.
  • the odor equipment 250 is equipment for odorizing the natural gas by adding an odorant to the natural gas whose heat has been adjusted. Since the natural gas obtained by vaporizing the LNG is odorless, the odor equipment 250 odorizes this natural gas for safety.
  • Fig. 3 shows an example of a process flow of the processing apparatus 100 according to the present embodiment.
  • the processing apparatus 100 acquires the tank state of the LNG tank 210.
  • the tank state acquiring section 120 acquires the tank state of the LNG tank 210 from the DCS, via the network.
  • the tank state may be acquired from the data historian apparatus or the LNG calculator.
  • the tank state may be acquired from a PIMS (Plant Information Management System).
  • the tank state acquiring section 120 may acquire the tank state including stocks, the amount, heat, composition ratio, density, and the like of the stored LNG that is stored in the LNG tank 210.
  • the processing apparatus 100 acquires the liquefied raw material receiving operation scheduling.
  • the receiving operation scheduling acquiring section 110 acquires the LNG receiving operation scheduling via the network.
  • the receiving operation scheduling acquiring section 110 may acquire the receiving operation scheduling that has been determined by mathematical programming based on a dock scheduling, a delivery schedule, the tank state, and the like.
  • the receiving operation scheduling acquiring section 110 may acquire the receiving operation scheduling determined in consideration of various types of constraint conditions.
  • the dock scheduling may include the type of tanker, the tanker entry date and time, the LNG load amount, the loaded LNG production site, the heat amount, the composition ratio, the density, and the like, for example.
  • the delivery schedule may include the intended use (municipal gas, power generation, or the like), the amount of demand for other uses, and the like, for example.
  • the tank state may include the amount, heat, composition ratio, density, and the like of the stored LNG that is stored in each of the plurality of LNG tanks 210, for example, and may be the same as the tank state acquired at step S310.
  • the constraint conditions may include a tank constraint such as an upper limit value or a lower limit value for the capacity of the LNG tank 210 and whether mixing of different types of LNG is possible, a berth constraint (for the pier where tankers enter), a reception or storage priority designation, a maintenance plan, and the like, for example.
  • the receiving operation scheduling determined based on the above data may include a dock scheduling indicating which tanker is to be received at which station, a tank scheduling including a plan concerning at least one of which of the plurality of tanks the liquefied raw material is to be received in, which of the plurality of tanks the liquefied raw material is to be discharged from, and which of the plurality of tanks the liquefied raw material is to be transferred between, for example.
  • the receiving operation scheduling acquiring section 110 may acquire the receiving operation scheduling including which LNG tanker is to be received at which station, which LNG tank 210 among the plurality of LNG tanks 210 the LNG loaded in the tanker is to be received in, which LNG tank 210 among the plurality of LNG tanks 210 the LNG is to be discharged from, which LNG tanks 210 among the plurality of LNG tanks 210 the LNG is to be transferred between, and the like.
  • the processing apparatus 100 acquires the tank state and then acquires the receiving operation scheduling, but the present invention is not limited to this.
  • the processing apparatus 100 may instead acquire the receiving operation scheduling and then acquire the tank state.
  • the order of step 310 and step 320 may be reversed.
  • the processing apparatus 100 forecasts the future tank state of a LNG tank 210.
  • the forecasting model 130 receives, as input, the tank state of the LNG tank 210 acquired at step 310 and the LNG receiving operation scheduling acquired at step 320, and forecasts the future tank state of the LNG tank 210 based on at least a change of the LNG density in the tank.
  • the processing apparatus 100 may perform a CFD (Computational Fluid Dynamics) simulation, for example.
  • the forecasting model 130 may forecast the future tank state based on at least a change in the BOG (Boil Off Gas) obtained by vaporizing the liquefied raw material.
  • BOG Boil Off Gas
  • the stored LNG that is stored in the LNG tank 210 is naturally heated from outside the tank, generates the BOG (mainly methane) at a ratio of approximately 0.1% to 0.2% per day (% mass), and the concentration thereof increases over time (the ratio of heavy components increases), thereby increasing the density.
  • Reception methods are considered that take into consideration the density difference caused by the amount of such generated BOG changing over time, but the BOG generation rate also changes due to this difference.
  • the forecasting model 130 according to the present embodiment forecasts the future tank state based on the change of the BOG, that is, the change in density caused by the effect of the BOG, and therefore it is possible to forecast the future tank state in greater detail.
  • the forecasting model 130 may forecast the future tank state based on at least the density of the liquefied raw material stored in the tank and the density of the liquefied raw material to be received in the tank.
  • the forecasting model 130 forecasts the future tank state using a CFD simulation that takes into consideration a turbulent flow model or other physical models, with a mass conservation equation, a momentum conservation equation, and an energy conservation equation as governing equations.
  • the mixing of the two fluids that are the stored LNG and the received LNG, i.e. the change of the density of the LNG, and also the change in the density distribution of the LNG are dependent on parameters such as the stored LNG density, the received LNG density, and the stored LNG amount, for example.
  • the forecasting model 130 forecasts the future tank state in this manner based on the change in density and also the change in the density distribution, which take into consideration the density of the liquefied raw material stored in the tank, the density of the liquefied raw material to be received in the tank, and the amount of liquefied raw material stored in the tank, and therefore it is possible to forecast the future tank state in greater detail.
  • the forecasting model 130 forecasts the future tank state based on the change in density that takes into consideration such stratification or the change in the amount of BOG generated due to the reception method, and therefore it is possible to forecast the future tank state in greater detail.
  • the processing apparatus 100 dynamically simulates the tank state of the LNG tank 210 based on at least the change in density of the LNG, using the forecasting model 130, and therefore the forecasting model 130 can function as a mirror model that simulates a tank state equivalent to that of the actual LNG tank 210.
  • the processing apparatus 100 can then forecast the future tank state of the LNG tank 210, i.e. make a transient forecast of the tank state of the LNG tank 210, by performing the dynamic simulation from the current tank state.
  • the processing apparatus 100 determines whether to update the receiving operation scheduling. For example, the receiving operation scheduling updating section 140 judges whether or not to update the receiving operation scheduling based on the future tank state of the LNG tank 210 forecasted by the forecasting model 130. Then, if it is judged at step 340 that there is to be an update, the processing apparatus 100 updates the receiving operation scheduling, returns to step S310, and repeats the processing.
  • the receiving operation scheduling updating section 140 may update the receiving operation scheduling such as the tank scheduling that includes a plan concerning at least one of which of the plurality of tanks is to receive the liquefied raw material, which of the plurality of tanks is to discharge the liquefied raw material, and which of the plurality of tanks the liquefied raw material is to be transferred between, such that the future tank state satisfies a constraint for the heat, composition ratio, density, or the like of the stored LNG, for example.
  • the plan concerning which of the plurality of tanks is to receive the liquefied raw material or the like may be updated. Furthermore, in a case where the different types of LNG having a large density difference between are to be mixed, for example, it is forecasted that stratification or roll over will occur, and the plan concerning which of the plurality of tanks is to receive the liquefied raw material may be updated such that the amount of BOG generated does not exceed a forecasted BOG consumption amount.
  • the processing apparatus 100 outputs the operation plan of the plant 200 in accordance with the final receiving operation scheduling.
  • the operation plan output section 150 may output the operation plan to the DCS via the network, for example.
  • the forecasting model 130 receives, as input, the tank state of the tank that stores the liquefied raw material and the liquefied raw material receiving operation scheduling, and forecasts the future tank state based on at least a change in the density of the liquefied raw material in the tank, and therefore it is possible to forecast the future tank state in greater detail.
  • the operation plan output section 150 then outputs the operation plan of the plant that includes the tank, based on the forecasted tank state, and therefore it is possible to output the operation plan of the plant that reflects the tank state forecasted by the forecasting model 130.
  • the processing apparatus 100 of the present embodiment the future tank state of the LNG tank 210 is forecasted in detail by performing a dynamic simulation from the current tank state, for example, and therefore it is possible to forecast the tank state in greater detail even in a case where different types of LNG are mixed and stored in a single tank.
  • the processing apparatus 100 updates the receiving operation scheduling, such as a tank scheduling, based on the forecasted future tank state, and therefore it is possible to minimize the risk of stratification and roll over in advance.
  • Fig. 4 shows an example of a block diagram of the processing apparatus 100 according to a variation of the present embodiment.
  • the processing apparatus 100 further includes a flow rate calculating section 410.
  • the flow rate calculating section 410 is connected to the output of the forecasting model 130 and calculates the flow rate of the additive raw material that is added to the liquefied raw material, based on the tank state forecasted by the forecasting model 130.
  • the flow rate calculating section 410 directly or indirectly (e.g. from the forecasted composition ratio or density of the LNG) estimates the heat [MJ/m 3 ] per unit volume of the discharged LNG, based on the tank state forecasted by the forecasting model 130.
  • the flow rate calculating section 410 calculates the mixing ratio of LNG and LPG that causes the gas that is to be supplied to have the output heat (approximately 40 to 45 MJ/m 3 N) of municipal gas, from the heat (approximately 100 MJ/m 3 ) per unit volume of the LPG and the estimated heat per unit volume of the discharged LNG.
  • the flow rate calculating section 410 then calculates the flow rate of the additive LPG necessary for adjusting the heat in order to supply the gas as municipal gas, from the mixing ratio of the LNG and LPG.
  • the flow rate calculating section 410 supplies the receiving operation scheduling updating section 140 with the additive raw material flow rate calculated in this manner.
  • the receiving operation scheduling updating section 140 updates the liquefied raw material receiving operation scheduling using an objective function that includes at least the additive raw material flow rate calculated by the flow rate calculating section 410 in a component thereof. Furthermore, the receiving operation scheduling updating section 140 may update the receiving operation scheduling of the liquefied raw material in a manner to minimize the work relating to the LNG tank. As an example, the processing apparatus 100 repeatedly performs the processes of forecasting the tank state, calculating the additive raw material flow rate, and updating the receiving operation scheduling, and the receiving operation scheduling updating section 140 determines the plan that minimizes this objective function to be the final liquefied raw material receiving operation scheduling.
  • the receiving operation scheduling updating section 140 may update the liquefied raw material receiving operation scheduling in a manner to minimize the additive raw material flow rate calculated by the flow rate calculating section 410. Specifically, the receiving operation scheduling updating section 140 may determine the plan that minimizes the objective function to be the final liquefied raw material receiving operation scheduling, using only the additive raw material flow rate as a component in the objective function. Therefore, according to the processing apparatus 100 of the present modification, it is possible to reduce the cost incurred when generating the municipal gas, by updating the receiving operation scheduling in a manner to reduce the amount of LPG added to the LNG.
  • Fig. 5 shows an example of a block diagram of the processing apparatus 100 according to another modification of the present embodiment.
  • the processing apparatus 100 includes a discharge state acquiring section 510 and a model updating section 520.
  • the discharge state acquiring section 510 acquires the discharge state of the liquefied raw material at the time that the liquefied raw material is actually discharged from the tank.
  • the discharge state acquiring section 510 acquires an actual measured value of at least one of the heat, composition ratio, and density of the discharged LNG that is measured directly from the LNG that has actually been discharged from the tank.
  • the discharge state acquiring section 510 acquires the discharge state from the plant that is a control target via the network.
  • the discharge state acquiring section 510 may receive a keyboard manipulation, mouse manipulation, and the like performed by a user to acquire the discharge state via user input, or may acquire the discharge state via a memory device or the like capable of storing data.
  • the discharge state acquiring section may acquire the discharge state from the DCS.
  • the discharge state may be acquired from a data historian apparatus or an LNG calculator.
  • the discharge state may be acquired from a PIMS (Plant Information Management System).
  • the discharge state acquiring section 510 supplies the model updating section 520 with the acquired discharge state.
  • the model updating section 520 is connected to the output of the forecasting model 130 and updates the forecasting model based on the discharge state of the liquefied raw material and the forecasted tank state. As an example, the model updating section 520 calculates the error between the actual measured value of at least one of the heat, composition ratio, and density of the discharged LNG that is measured directly from the LNG that has actually been discharged from the tank and the estimated value of at least one of the heat, composition ratio, and density of the discharged LNG that is estimated from the tank state forecasted by the forecasting model 130. The model updating section 520 then updates the forecasting model 130 in a manner to decrease this error, using this error as an error function.
  • the forecasting model 130 is updated by comparing the tank state forecasted by the forecasting model 130 and the discharge state of the liquefied raw material that is actually discharged from the tank, and therefore it is possible to tune the forecasting model 130 in a manner to decrease the gap between the actual value and the estimated value output by the forecasting model 130.
  • the processing apparatus 100 updates the forecasting model 130 based on the discharge state of the liquefied raw material and the forecasted tank state
  • the learning model realized by machine learning may be used as the forecasting model 130.
  • the forecasting model 130 may be a model realized by machine learning that receives, as input, the tank state of the tank storing the liquefied raw material and the liquefied raw material receiving operation scheduling, based on the discharge state of the liquefied raw material and the forecasted tank state, and outputs the future tank state.
  • Various embodiments of the present invention may be described with reference to flowcharts and block diagrams whose blocks may represent (1) steps of processes in which operations are performed or (2) sections of apparatuses responsible for performing operations. Certain steps and sections may be implemented by dedicated circuitry, programmable circuitry supplied with computer-readable instructions stored on computer-readable media, and/or processors supplied with computer-readable instructions stored on computer-readable media.
  • Dedicated circuitry may include digital and/or analog hardware circuits and may include integrated circuits (IC) and/or discrete circuits.
  • Programmable circuitry may include reconfigurable hardware circuits comprising logical AND, OR, XOR, NAND, NOR, and other logical operations, flip-flops, registers, memory elements, etc., such as field-programmable gate arrays (FPGA), programmable logic arrays (PLA), etc.
  • FPGA field-programmable gate arrays
  • PLA programmable logic arrays
  • Computer-readable media may include any tangible device that can store instructions for execution by a suitable device, such that the computer-readable medium having instructions stored therein comprises an article of manufacture including instructions which can be executed to create means for performing operations specified in the flowcharts or block diagrams.
  • Examples of computer-readable media may include an electronic storage medium, a magnetic storage medium, an optical storage medium, an electromagnetic storage medium, a semiconductor storage medium, etc.
  • Computer-readable media may include a floppy disk, a diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an electrically erasable programmable read-only memory (EEPROM), a static random access memory (SRAM), a compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a BLU-RAY(registered trademark) disc, a memory stick, an integrated circuit card, etc.
  • RAM random access memory
  • ROM read-only memory
  • EPROM or Flash memory erasable programmable read-only memory
  • EEPROM electrically erasable programmable read-only memory
  • SRAM static random access memory
  • CD-ROM compact disc read-only memory
  • DVD digital versatile disk
  • BLU-RAY registered trademark
  • Computer-readable instructions may include assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, JAVA (registered trademark), C++, etc., and conventional procedural programming languages, such as the "C" programming language or similar programming languages.
  • ISA instruction-set-architecture
  • Machine instructions machine dependent instructions
  • microcode firmware instructions
  • state-setting data or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, JAVA (registered trademark), C++, etc., and conventional procedural programming languages, such as the "C" programming language or similar programming languages.
  • Computer-readable instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, or to programmable circuitry, locally or via a local area network (LAN), wide area network (WAN) such as the Internet, etc., to execute the computer-readable instructions to create means for performing operations specified in the flowcharts or block diagrams.
  • processors include computer processors, processing units, microprocessors, digital signal processors, controllers, microcontrollers, etc.
  • Fig. 6 shows an example of a computer 2200 in which aspects of the present invention may be wholly or partly embodied.
  • a program that is installed in the computer 2200 can cause the computer 2200 to function as or perform operations associated with apparatuses of the embodiments of the present invention or one or more sections thereof, and/or cause the computer 2200 to perform processes of the embodiments of the present invention or steps thereof.
  • Such a program may be executed by the CPU 2212 to cause the computer 2200 to perform certain operations associated with some or all of the blocks of flowcharts and block diagrams described herein.
  • the computer 2200 includes a CPU 2212, a RAM 2214, a graphic controller 2216, and a display device 2218, which are mutually connected by a host controller 2210.
  • the computer 2200 also includes input/output units such as a communication interface 2222, a hard disk drive 2224, a DVD-ROM drive 2226 and an IC card drive, which are connected to the host controller 2210 via an input/output controller 2220.
  • the computer also includes legacy input/output units such as a ROM 2230 and a keyboard 2242, which are connected to the input/output controller 2220 through an input/output chip 2240.
  • the CPU 2212 operates according to programs stored in the ROM 2230 and the RAM 2214, thereby controlling each unit.
  • the graphic controller 2216 obtains image data generated by the CPU 2212 on a frame buffer or the like provided in the RAM 2214 or in itself, and causes the image data to be displayed on the display device 2218.
  • the communication interface 2222 communicates with other electronic devices via a network.
  • the hard disk drive 2224 stores programs and data used by the CPU 2212 within the computer 2200.
  • the DVD-ROM drive 2226 reads the programs or the data from the DVD-ROM 2201, and provides the hard disk drive 2224 with the programs or the data via the RAM 2214.
  • the IC card drive reads programs and data from an IC card, and/or writes programs and data into the IC card.
  • the ROM 2230 stores therein a boot program or the like executed by the computer 2200 at the time of activation, and/or a program depending on the hardware of the computer 2200.
  • the input/output chip 2240 may also connect various input/output units via a parallel port, a serial port, a keyboard port, a mouse port, and the like to the input/output controller 2220.
  • a program is provided by computer readable media such as the DVD-ROM 2201 or the IC card.
  • the program is read from the computer readable media, installed into the hard disk drive 2224, RAM 2214, or ROM 2230, which are also examples of computer readable media, and executed by the CPU 2212.
  • the information processing described in these programs is read into the computer 2200, resulting in cooperation between a program and the above-mentioned various types of hardware resources.
  • An apparatus or method may be constituted by realizing the operation or processing of information in accordance with the usage of the computer 2200.
  • the CPU 2212 may execute a communication program loaded onto the RAM 2214 to instruct communication processing to the communication interface 2222, based on the processing described in the communication program.
  • the communication interface 2222 under control of the CPU 2212, reads transmission data stored on a transmission buffering region provided in a recording medium such as the RAM 2214, the hard disk drive 2224, the DVD-ROM 2201, or the IC card, and transmits the read transmission data to a network or writes reception data received from a network to a reception buffering region or the like provided on the recording medium.
  • the CPU 2212 may cause all or a necessary portion of a file or a database to be read into the RAM 2214, the file or the database having been stored in an external recording medium such as the hard disk drive 2224, the DVD-ROM drive 2226 (DVD-ROM 2201), the IC card, etc., and perform various types of processing on the data on the RAM 2214.
  • the CPU 2212 may then write back the processed data to the external recording medium.
  • the CPU 2212 may perform various types of processing on the data read from the RAM 2214, which includes various types of operations, processing of information, condition judging, conditional branch, unconditional branch, search/replace of information, etc., as described throughout this disclosure and designated by an instruction sequence of programs, and writes the result back to the RAM 2214.
  • the CPU 2212 may search for information in a file, a database, etc., in the recording medium.
  • the CPU 2212 may search for an entry matching the condition whose attribute value of the first attribute is designated, from among the plurality of entries, and read the attribute value of the second attribute stored in the entry, thereby obtaining the attribute value of the second attribute associated with the first attribute satisfying the predetermined condition.
  • the above-explained program or software modules may be stored in the computer readable media on or near the computer 2200.
  • a recording medium such as a hard disk or a RAM provided in a server system connected to a dedicated communication network or the Internet can be used as the computer readable media, thereby providing the program to the computer 2200 via the network.

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Abstract

Il est préférable de prévoir en détail l'état futur d'un réservoir qui stocke une matière première liquéfiée, et d'établir un plan de fonctionnement d'une installation qui comprend ce réservoir. L'invention concerne un appareil de traitement comprenant un modèle de prévision qui reçoit, en tant qu'entrée, un état d'un réservoir qui stocke une matière première liquéfiée et une planification d'opération de réception de la matière première liquéfiée, et prévoit l'état du réservoir dans le futur sur la base d'au moins un changement d'une densité de la matière première liquéfiée dans le réservoir; et une section de sortie de plan de fonctionnement qui délivre en sortie un plan de fonctionnement d'une installation comprenant le réservoir, sur la base de l'état du réservoir prévu.
PCT/JP2020/003013 2019-03-27 2020-01-28 Appareil, procédé et programme de traitement WO2020195120A1 (fr)

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CN115681821A (zh) * 2022-12-13 2023-02-03 成都秦川物联网科技股份有限公司 用于智慧燃气设备管理的加臭自动控制方法和物联网系统
KR102539754B1 (ko) * 2022-04-30 2023-06-05 (주)넵스이엔씨 액화가스 저장탱크 롤오버 예측 및 통합관리 시스템

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JP7302414B2 (ja) * 2019-09-30 2023-07-04 横河電機株式会社 システム、方法、および、プログラム

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JP2013164672A (ja) 2012-02-09 2013-08-22 Tokyo Gas Co Ltd 受入計画策定方法、及び受入計画策定システム
EP2772866A1 (fr) * 2011-10-24 2014-09-03 Osaka Gas Co., Ltd. Système et procédé d'élaboration d'un plan d'exploitation de cuves de stockage
WO2018189789A1 (fr) * 2017-04-10 2018-10-18 日本郵船株式会社 Procédé d'estimation d'état de réservoir et programme d'estimation d'état de réservoir

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JPH02138600A (ja) * 1988-11-16 1990-05-28 Tokyo Gas Co Ltd 液化ガス貯槽の運転方法
EP2772866A1 (fr) * 2011-10-24 2014-09-03 Osaka Gas Co., Ltd. Système et procédé d'élaboration d'un plan d'exploitation de cuves de stockage
JP2013164672A (ja) 2012-02-09 2013-08-22 Tokyo Gas Co Ltd 受入計画策定方法、及び受入計画策定システム
WO2018189789A1 (fr) * 2017-04-10 2018-10-18 日本郵船株式会社 Procédé d'estimation d'état de réservoir et programme d'estimation d'état de réservoir

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Publication number Priority date Publication date Assignee Title
KR102539754B1 (ko) * 2022-04-30 2023-06-05 (주)넵스이엔씨 액화가스 저장탱크 롤오버 예측 및 통합관리 시스템
CN115681821A (zh) * 2022-12-13 2023-02-03 成都秦川物联网科技股份有限公司 用于智慧燃气设备管理的加臭自动控制方法和物联网系统

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