System for managing ammonia decomposition plant, and method therefor

Abstract

To provide a system for guaranteeing a stable operation of an ammonia decomposition plant under an emergency condition, and to provide a method therefor.SOLUTION: A system according to the present disclosure comprises: a model-producing module for producing a digital twin model including a replica model of a physical constituent element of an ammonia decomposition plant; an input module for inputting a kinetic energy request amount and output information of an ammonia decomposition device included in the ammonia decomposition plant into the digital twin model; an emergency condition imposition module for imposing an emergency condition on the replica model of the physical constituent element with respect to a preset emergency scenario; a data collection comparison module for comparing first data when the emergency condition is imposed with second data when no emergency condition is imposed, by collecting data from the digital twin model under the imposed emergency condition; and a scenario-producing module for producing a control scenario for the preset emergency scenario based on a result of comparing the first data with the second data.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to a system and method for managing an ammonia cracking plant. [Background technology]

[0002] In recent years, as various environmental issues have emerged as major problems around the world, there has been an increasing demand for the introduction of environmentally friendly alternative energy sources to replace existing internal combustion engines not only in general vehicles but also in heavy machinery such as excavators. As a result, fuel cell systems that use fuel cells (FCs) as a power source have been attracting attention as an environmentally friendly alternative energy source.

[0003] To ensure smooth operation and high performance of a fuel cell system, the Energy Management System (EMS) plays an important role in determining how to properly distribute power demand among power sources, and this requires stable and reliable EMS operation.

[0004] In a situation where there are multiple pipes, when the flow rate is concentrated in a specific pipe, it is necessary to equalize the flow rate by automatically reducing the flow rate of the corresponding pipe. Existing methods have difficulty in accounting for the complex interactions between different components of the system. To solve this problem, Digital Twin (DT) is being researched to provide an accurate real-time simulation of the entire physical system. Summary of the Invention [Problem to be solved by the invention]

[0005] According to one aspect of the present disclosure, a system and method for ensuring stable operation of an ammonia decomposition plant under emergency conditions can be provided. [Means for solving the problem]

[0006] A system according to one embodiment of the present disclosure may include a model generation module that generates a digital twin model including a replica model of physical components of an ammonia cracking plant; an input module that inputs dynamic energy demand and output information of an ammonia cracker included in the ammonia cracking plant to the digital twin model; an emergency condition imposition module that imposes emergency conditions on the replica model of the physical components for a preset emergency scenario; a data collection and comparison module that collects data from the digital twin model under the imposed emergency condition and compares first data when the emergency condition is imposed with second data when the emergency condition is not imposed; and a scenario generation module that generates a control scenario for the preset emergency scenario based on a comparison result between the first data and the second data.

[0007] According to one embodiment, the physical components of the ammonia cracking plant include the ammonia cracker, sensors, and controllers, and the model generation module can update the digital twin model with real-time data from the ammonia cracking plant.

[0008] According to one embodiment, the input module can compare the difference between the dynamic energy demand actually required by the digital twin model and the output of the output information of the ammonia cracking plant.

[0009] According to one embodiment, the emergency condition imposition module can set multiple virtual emergencies in the digital twin model to prepare for potential emergency situations, evaluate the response and performance of the digital twin model in the set virtual emergencies, and predict adjustments necessary to ensure the safety and stability of the ammonia cracking plant.

[0010] According to one embodiment, the data collection and comparison module can identify potential weaknesses in the system in each emergency and the degree of vulnerability for each replication model based on the data.

[0011] In one embodiment, the scenario generation module may use the comparison data between the first data and the second data to generate control scenarios for potential emergency situations.

[0012] According to one embodiment, the digital twin model may include a plant control unit corresponding to a replica model of the control device that controls the ammonia decomposition device, and a data collection unit that collects the real-time data.

[0013] According to one embodiment, the physical components of the ammonia decomposition plant may further include a feed blower, a flow control valve, and at least one reaction unit, and the sensors may include a temperature sensor and a pressure sensor.

[0014] According to one embodiment, the at least one reaction unit may include a plurality of reaction sections that receive a flow rate of a feed for a reforming reaction and a flow rate of a feed for a combustion reaction, and a combustion section that outputs a combustion flue gas.A method according to one embodiment of the present disclosure may include a step of generating a digital twin model including a replica model of a physical component of an ammonia cracking plant, a step of inputting a dynamic energy demand and output information of an ammonia cracker included in the ammonia cracking plant to the digital twin model, a step of imposing an emergency condition on the replica model of the physical component for a preset emergency scenario, a step of collecting data from the digital twin model under the imposed emergency condition, a step of comparing first data when the emergency condition is imposed with second data when the emergency condition is not imposed, and a step of generating a control scenario for the preset emergency scenario based on a comparison result between the first data and the second data. Effect of the Invention

[0015] According to one embodiment of the present disclosure, the stable operation of an ammonia decomposition plant is ensured under emergency conditions, thereby improving the reliability of the system.

[0016] Additionally, according to one embodiment of the present disclosure, by designing a digital twin model to closely mimic the operation of a plant under various conditions, there are real-time monitoring, analysis, and optimization effects.

[0017] In addition, according to an embodiment of the present disclosure, appropriate action scenarios to be implemented in response to specific emergency situations are pre-constructed, thereby ensuring the safety of the system.

[0018] Additionally, an embodiment of the present disclosure may have the effect of helping to identify potential areas for improvement, ultimately contributing to the overall sustainability and cost-effectiveness of the plant. [Brief description of the drawings]

[0019] [Figure 1] FIG. 1 is a diagram for explaining a system according to an embodiment of the present disclosure. [Diagram 2] FIG. 2 is a diagram illustrating a relationship between a plant and a digital twin model according to an embodiment of the present disclosure. [Diagram 3] FIG. 3 is a diagram for explaining a method according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0020] Hereinafter, embodiments of the present invention will be described in more detail with reference to the drawings. However, these embodiments are merely illustrative of the present invention and are not intended to limit the present invention.

[0021] The same reference numerals refer to the same components throughout this disclosure. This disclosure does not describe all elements of the embodiments, and general content in the technical field to which the disclosure belongs or content that is duplicated in the embodiments will be omitted. The term "part, module, member, block" used in the specification can be embodied in software or hardware, and depending on the embodiment, multiple "parts, modules, members, blocks" may be embodied as one component, or one "part, module, member, block" may include multiple components.

[0022] Throughout the specification, when a part is referred to as being "coupled" to another part, this includes not only direct coupling but also indirect coupling, including coupling via a wireless communication network.

[0023] Furthermore, unless otherwise specified, when a part "comprises" a certain component, it does not exclude other components but means that the part may further include other components.

[0024] Throughout the specification, a member is referred to as being "on" another member not only when the member is in contact with the other member, but also when there is another member between the two members.

[0025] The terms "first", "second", etc. are used only to distinguish one component from another component, and are not intended to limit the components.

[0026] The singular expression includes the plural expression unless the context clearly indicates otherwise.

[0027] The identification numbers in each step are used for ease of description and do not dictate the order of each step, and each step may be performed in a manner different from the stated order unless the context clearly dictates a particular order.

[0028] In this specification, the term "device according to the present disclosure" includes all of various devices capable of performing computation and providing a result to a user. For example, the device according to the present disclosure may include all of a computer, a server device, and a portable terminal, and may be in any form.

[0029] Here, the computer may include, for example, a notebook computer, a desktop computer, a laptop computer, a tablet PC, a slate PC, etc. equipped with a web browser.

[0030] The server device is a server that communicates with external devices and processes information, and may include an application server, a computing server, a database server, a file server, a game server, a mail server, a proxy server, a web server, and the like.

[0031] The portable terminal is, for example, a wireless communication device that ensures portability and mobility, and may include all kinds of handheld-based wireless communication devices such as PCS (Personal Communication System), GSM (Global System for Mobile communications), PDC (Personal Digital Cellular), PHS (Personal Handyphone System), PDA (Personal Digital Assistant), IMT (International Mobile Telecommunication)-2000, CDMA (Code Division Multiple Access)-2000, W-CDMA (W-Code Division Multiple Access), WiBro (Wireless Broadband Internet) terminals, and smartphones, as well as wearable devices such as watches, rings, bracelets, anklets, necklaces, glasses, contact lenses, or head-mounted devices (HMDs).

[0032] FIG. 1 is a diagram for explaining a system according to an embodiment of the present disclosure.

[0033] 1 , a system 100 can manage an ammonia cracking plant. The system 100 can include a physical plant 110, a model generation module 121, an input module 122, an emergency condition imposition module 123, a data collection and comparison module 124, a scenario generation module 125, and a digital twin model 130.

[0034] The physical plant 110 may be an ammonia decomposition plant, but is not limited thereto. The physical plant 110 embodied in the ammonia decomposition plant may include an ammonia decomposition device 111, a control device 112 for controlling the ammonia decomposition device 111, a feed blower 113, a flow control valve 114, a reaction unit 115, a temperature sensor 116, and a pressure sensor 117. The temperature sensor 116 or the pressure sensor 117 may be referred to as a sensor.

[0035] The model generation module 121 can generate the digital twin model 130. The digital twin model 130 can include a replica model of the physical components of the physical plant 110. The physical components of the physical plant 110 can be, for example, the physical components of an ammonia decomposition plant. In this case, the physical components of the physical plant 110 can include an ammonia decomposition device 111, a control device 112, a feed blower 113, a flow control valve 114, a reaction unit 115, a temperature sensor 116, and a pressure sensor 117. The replica models included in the digital twin model 130 can include an ammonia decomposition device 131, a control device 132, a feed blower 133, a flow control valve 134, a reaction unit 135, a temperature sensor 136, and a pressure sensor 137.

[0036] The model generation module 121 can continuously update the digital twin model 130 with real-time data from the ammonia cracking plant for more accurate and specific simulations and predictions.

[0037] The input module 122 can input the dynamic energy demand and output information of the physical plant 110 to the digital twin model 130. For example, the input module 122 can input the dynamic energy demand and output information of the ammonia cracker 111 included in the ammonia cracking plant to the digital twin model 130.

[0038] The input module 122 can compare the difference between the dynamic energy demand actually required by the digital twin model 130 and the output of the output information of the ammonia cracking plant.

[0039] Emergency condition imposition module 123 can impose emergency conditions on the replica models of the physical components of physical plant 110 for predefined emergency scenarios.

[0040] The emergency condition imposition module 123 can set a variety of virtual emergency situations in the digital twin model 130 to prepare for potential emergency situations. The emergency condition imposition module 123 can then evaluate the response and performance of the digital twin model 130 in the set virtual emergency situations. The emergency condition imposition module 123 can also predict adjustments required to ensure the safety and stability of the ammonia cracking plant.

[0041] The data collection and comparison module 124 can collect data from the digital twin model 130 under the imposed emergency condition. The data collection and comparison module 124 can then compare the first data when the emergency condition is imposed with the second data when the emergency condition is not imposed. Comparison data can be generated that includes a result of comparing the first data and the second data.

[0042] Based on the data, the data collection and comparison module 124 can identify weaknesses in the system 100 that may occur in each emergency situation and the degree of vulnerability for each replication model.

[0043] The scenario generation module 125 can generate a control scenario for a pre-defined emergency scenario based on the comparison data.

[0044] The scenario generation module 125 can use the comparison data between the first data and the second data to generate control scenarios for a variety of potential emergency situations.

[0045] As described above, the present invention has the effect of improving the reliability of the system by ensuring stable operation of the ammonia decomposition plant under emergency conditions.

[0046] Also, as mentioned above, by designing a digital twin model to closely mimic the operation of a plant under a variety of conditions, there are real-time monitoring, analysis, and optimization effects.

[0047] In addition, as described above, by constructing appropriate action scenarios to be implemented in response to specific emergency situations in advance, the safety of the system can be ensured.

[0048] Additionally, the foregoing has the effect of helping to identify potential areas for improvement, ultimately contributing to the overall sustainability and cost-effectiveness of the plant.

[0049] FIG. 2 is a diagram illustrating a relationship between a plant and a digital twin model according to an embodiment of the present disclosure.

[0050] Referring to FIG. 2, the relationship diagram 200 may include a dynamic load input 210, a digital twin model 220, first to Nth reaction units (230_1, 230_N), first and second blowers (241, 242), first to fourth control valves (251, 252, 253, 254), and first to tenth information (I1, I2, I3, I4, I5, I6, I7, I8, I9, I10).

[0051] The dynamic load input 210 may correspond to the dynamic energy demands discussed above. The dynamic load input 210 may be input to the digital twin model 220.

[0052] The digital twin model 220 may include a plant control unit 221 corresponding to a replica model for a control device that controls an ammonia decomposition device, and a data collection unit 222 that collects real-time data. The data collection unit 222 may receive a dynamic load input 210 and inputs of first to tenth information (I1, I2, I3, I4, I5, I6, I7, I8, I9, I10). The data collection unit 222 may receive inputs of a feed flow rate for a reforming reaction and a feed flow rate for a combustion reaction provided via first to fourth control valves (251, 252, 253, 254).

[0053] The number of the first to N-th reaction units (230_1, 230_N) may be N as shown in FIG. 2. N may be an integer of 2 or more. However, the number is not limited thereto. The first reaction unit (230_1) may include a first reaction section (231_1), a combustion section (232_1), and a second reaction section (233_1). The N-th reaction unit (230_N) may include a first reaction section (231_N), a combustion section (232_N), and a second reaction section (233_N). The plurality of reaction sections may receive a feed flow rate for a reforming reaction and a feed flow rate for a combustion reaction. The first reaction section (231_1) may receive a reforming reaction feed. The first reaction section (231_1) may output a ninth information (I9). The combustion section (232_1) may receive a combustion reaction feed. The combustion unit (232_1) may output combustion flue gas and tenth information (I10). The second reaction unit (233_1) may output sixth information (I6) and reforming reaction products. The first reaction unit (231_N) may output seventh information (I7). The combustion unit (232_N) may receive a combustion reaction feed. The combustion unit (232_N) may output combustion flue gas and eighth information (I8). The second reaction unit (233_N) may output fifth information (I5) and reforming reaction products.

[0054] The reforming reaction feed may be input to the first blower 241. The first blower 241 may provide the reforming reaction feed to the first and third control valves (251, 253). The combustion reaction feed may be input to the second blower 242. The combustion reaction feed may include fuel and air. The second blower 242 may provide the combustion reaction feed to the second and fourth control valves (252, 254).

[0055] 3 is a diagram for explaining a method according to an embodiment of the present disclosure. Referring to FIG. 3, a method according to an embodiment of the present disclosure can manage an ammonia cracking plant.

[0056] A step is performed of generating a digital twin model that includes replica models of the physical components of the ammonia cracking plant (S100).

[0057] With regard to step S100, for example, with reference to FIG. 1, the model generation module 121 can generate a digital twin model 130 including replica models of the physical components of the physical plant 110 (e.g., an ammonia cracker 131, a controller 132, a feed blower 133, a flow control valve 134, a reaction unit 135, a temperature sensor 136, and a pressure sensor 137, etc.), and continuously update the digital twin model 130 with real-time data of the ammonia cracking plant for more accurate and specific simulation and prediction.

[0058] A step of inputting dynamic energy demand and output information of an ammonia decomposition unit included in an ammonia decomposition plant into the digital twin model is performed (S200).

[0059] Regarding step S200, for example, with reference to FIG. 1 , the input module 122 can input a dynamic energy requirement to the digital twin model 130, input the output information of the ammonia decomposition device 111 included in the ammonia decomposition plant, and compare the difference between the dynamic energy requirement actually required by the digital twin model 130 and the output amount of the output information of the ammonia decomposition plant.

[0060] A step of imposing an emergency condition on the replica model of the physical component for a preset emergency scenario is performed (S300).

[0061] 1, for example, the emergency condition imposition module 123 can set a variety of virtual emergency situations in the digital twin model 130 to prepare for potential emergency situations. The emergency condition imposition module 123 can then evaluate the response and performance of the digital twin model 130 in the set virtual emergency situations. The emergency condition imposition module 123 can also predict adjustments required to ensure the safety and stability of the ammonia cracking plant.

[0062] Under the imposed emergency condition, a step of collecting data from the digital twin model is performed (S400).

[0063] A step of comparing first data when an emergency condition is imposed with second data when no emergency condition is imposed is performed (S500).

[0064] Regarding steps S400 and S500, for example, with reference to FIG. 1, the data collection and comparison module 124 collects data from the digital twin model 130 under an imposed emergency condition, compares first data when the emergency condition is imposed with second data when the emergency condition is not imposed, and generates comparison data including the results of comparing the first data and the second data, thereby identifying weaknesses of the system 100 that may occur in each emergency and the degree of vulnerability for each replication model.

[0065] A step of generating a control scenario for a preset emergency scenario based on the comparison data is performed (S600).

[0066] Regarding step S600, for example, with reference to FIG. 1, the scenario generation module 125 can generate a control scenario for a pre-defined emergency scenario based on the comparison data, and generate control scenarios for a variety of potential emergency situations using the comparison data between the first data and the second data.

[0067] Meanwhile, the disclosed embodiments may be embodied in the form of a storage medium storing instructions executable by a computer. The instructions may be stored in the form of program code, and when executed by a processor, may generate program modules to perform the operations of the disclosed embodiments. The storage medium may be embodied as a computer-readable storage medium.

[0068] The computer-readable storage medium includes any kind of storage medium in which an instruction code that can be decoded by a computer is stored. For example, it may be a ROM (Read Only Memory), a RAM (Random Access Memory), a magnetic tape, a magnetic disk, a flash memory, an optical data storage device, etc. As described above, the disclosed embodiments have been described with reference to the accompanying drawings. A person having ordinary skill in the art to which the present disclosure pertains should understand that the present disclosure can be implemented in a form different from the disclosed embodiments without changing the technical idea or essential features of the present disclosure. It should be understood that the disclosed embodiments are illustrative and not limiting.

Claims

1. 1. A system for managing an ammonia cracking plant, comprising: a model generation module for generating a digital twin model including a replica model of a physical component of the ammonia cracking plant; An input module for inputting dynamic energy demand and output information of an ammonia cracker included in the ammonia cracking plant into the digital twin model; an emergency condition imposition module that imposes an emergency condition on the replica model of the physical component for a preset emergency scenario; A data collection and comparison module that collects data from the digital twin model under the imposed emergency condition and compares first data when the emergency condition is imposed with second data when the emergency condition is not imposed; a scenario generation module that generates a control scenario for the preset emergency scenario based on a comparison result between the first data and the second data.

2. The physical components of the ammonia cracking plant include: The ammonia decomposition device, a sensor, and a control device, The model generation module includes:

2. The system of claim 1, further comprising: updating the digital twin model with real-time data from the ammonia cracking plant.

3. The input module includes:

2. The system of claim 1, further comprising: comparing a difference between the dynamic energy demand actually required by the digital twin model and an output amount of the output information of the ammonia cracking plant.

4. The emergency condition imposition module: Setting multiple virtual emergencies in the digital twin model to prepare for potential emergency situations; Evaluating the response and performance of the digital twin model in the virtual emergency situation; 10. The system of claim 1, wherein the system predicts necessary adjustments to ensure safety and stability of the ammonia cracking plant.

5. The data collection and comparison module includes: The system of claim 1 , further comprising: a network for identifying potential weaknesses in the system and a degree of vulnerability for each replication model that may occur in each emergency based on the data.

6. The scenario generation module includes: The system of claim 1 , further comprising: generating control scenarios for potential emergency situations using comparison data between the first data and the second data.

7. The digital twin model is a plant control unit corresponding to a replica model of the control device that controls the ammonia decomposition device; and a data collector for collecting the real-time data.

8. The physical components of the ammonia cracking plant include: Further comprising a feed blower, a flow control valve, and at least one reaction unit; The system of claim 2 , wherein the sensors include a temperature sensor and a pressure sensor.

9. The at least one reaction unit comprises:

10. The system of claim 8, comprising a plurality of reaction sections receiving a flow rate of feed for a reforming reaction and a flow rate of feed for a combustion reaction, and a combustion section outputting combustion flue gas.

10. 1. A method for operating an ammonia cracking plant, comprising: generating a digital twin model including replica models of physical components of the ammonia cracking plant; Inputting a dynamic energy demand and output information of an ammonia cracker included in the ammonia cracking plant into the digital twin model; imposing an emergency condition on the replica model of the physical component for a preset emergency scenario; collecting data from the digital twin model under the imposed emergency condition; comparing first data when the emergency condition is imposed with second data when the emergency condition is not imposed; generating a control scenario for the preset emergency scenario based on a comparison result between the first data and the second data.

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