System and method for optimizing flow of ammonia

Abstract

To provide a system and a method for optimizing a flow of ammonia, which activates a digital twin technique.SOLUTION: A system according to the present disclosure can contain: a physical plant; a digital twin technique against the physical plant; a data collection module that acquires a real time data from the physical plant; a simulation model that simulates at least one scenario by using a digital twin model, and identifies an optimization operation condition against a flow of a gas of ammonia on the basis of a simulation result; a feedback loop that realizes a feedback loop between the digital twin model and the physical plant in order to adjust the physical plant on the basis of the simulation result; and a performance monitoring module that monitors a performance of the physical plant in order to track an efficiency of an optimization process.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to systems and methods for optimizing ammonia flow. [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 distribute power demand between power sources. 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, it is possible to provide a system and method for controlling a flow rate in a specific pipe among a plurality of pipes where the flow rate is concentrated.

[0006] Additionally, according to one aspect of the present disclosure, a system and method for optimizing ammonia flow utilizing digital twin technology can be provided. [Means for solving the problem]

[0007] A system according to one embodiment of the present disclosure may include a physical plant, a digital twin model for the physical plant, a data collection module for acquiring real-time data from the physical plant, a simulation module for simulating at least one scenario using the digital twin model and identifying optimal operating conditions for the ammonia gas flow based on the simulation results, a feedback loop for implementing a feedback loop between the digital twin model and the physical plant to adjust the physical plant based on the simulation results, and a performance monitoring module for monitoring the performance of the physical plant to track the efficiency of the optimization process.

[0008] According to one embodiment, the physical plant may include pipes, valves, pumps, and sensors.

[0009] According to one embodiment, the data collection module may collect the real-time data from the sensors, the real-time data including at least one of temperature, pressure, flow rate, pump speed, pipe characteristics, and valve position.

[0010] According to one embodiment, the simulation module can minimize energy consumption of the system and maximize throughput handled by the system by adjusting operating conditions including at least one of the valve positions, the pump speed, and the pipe characteristics.

[0011] In one embodiment, the feedback loop can adjust at least one of pipes, valves, pumps, and sensors included in the physical plant in real time based on the simulation results.

[0012] According to one embodiment, the performance monitoring module can continuously monitor the performance of the physical plant and the digital twin model to determine whether the performance of the physical plant and the digital twin model meet pre-set targets.

[0013] According to an embodiment, the system may further include an update module that continuously updates the system based on the real-time data of the physical plant to ensure validity.

[0014] According to an embodiment, the system may further include a feedback module that adjusts the physical plant in response to optimized settings identified by the digital twin model.

[0015] According to one embodiment, the simulation module collects inputs indicating the amount of energy required and outputs of the physical plant from the digital twin model, collects from the digital twin model the flow rate of the feed for the reforming reaction, the flow rate of the feed for the combustion reaction, and specific activity information of the catalyst of each reaction unit where the reforming reaction and the combustion reaction occur, and can adjust the opening ratio of the feed flow control valve based on the collected information as an optimized scenario.

[0016] A method according to an embodiment of the present disclosure may include obtaining real-time data from a physical plant; simulating at least one scenario using a digital twin model for the physical plant; identifying optimal operating conditions for an ammonia gas flow based on simulation results; implementing a feedback loop between the digital twin model and the physical plant to adjust the physical plant based on the simulation results; and monitoring performance of the physical plant to track efficiency of an optimization process. Effect of the Invention

[0017] According to one embodiment of the present disclosure, optimizing the flow of a gas, such as ammonia, can have the effect of improving overall process efficiency, including maximizing ammonia throughput, minimizing energy consumption, and identifying optimal operating conditions.

[0018] Additionally, an embodiment of the present disclosure provides an improved environmental impact by optimizing gas flow, such as preventing the release of harmful emissions.

[0019] In addition, an embodiment of the present disclosure has the effect of improving system reliability by preventing potential system errors or malfunctions in the physical plant.

[0020] In addition, according to one embodiment of the present disclosure, by providing real-time response to changing conditions through a feedback loop between the digital twin model and the physical plant, it is possible to quickly take control actions, adjustment actions, tuning actions, etc. [Brief description of the drawings]

[0021] [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 one 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

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] The terms "first", "second", etc. are used only to distinguish one component from another component, and are not intended to limit the components.

[0028] The singular expression includes the plural expression unless the context clearly indicates otherwise.

[0029] 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.

[0030] In this specification, the term "device according to the present disclosure" includes all of the 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.

[0031] 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.

[0032] 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.

[0033] 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).

[0034] FIG. 1 is a diagram for explaining a system according to an embodiment of the present disclosure.

[0035] 1 , a system 100 can optimize ammonia gas flow in an industrial plant. The system 100 can include a physical plant 110, a data collection module 121, a simulation module 122, a feedback loop 123, a performance monitoring module 124, an update module 125, a feedback module 126, and a digital twin model 130.

[0036] Physical plant 110 may include, for example, pipes 111 , valves 112 , pumps 113 , and sensors 114 .

[0037] Data collection module 121 may obtain real-time data from physical plant 110. Data collection module 121 may obtain real-time data regarding various environments from sensors 114 included in physical plant 110. The real-time data may include, for example, at least one of temperature, pressure, flow rate, pump speed, pipe characteristics, and valve positions of physical plant 110.

[0038] The simulation module 122 can simulate at least one scenario using the digital twin model 130. The simulation module 122 can then identify optimal operating conditions for the ammonia gas flow based on the simulation results.

[0039] The simulation module 122 can minimize the energy consumption of the system 100 by adjusting operating conditions including at least one of valve positions, the pump speed, and the pipe characteristics. And, the simulation module 122 can maximize the throughput handled by the system 100.

[0040] The simulation module 122 can collect inputs indicating the amount of energy required in the system 100 and outputs of the physical plant 110 in the digital twin model 130. The simulation module 122 can collect the flow rate of a feed for a reforming reaction, the flow rate of the feed for a combustion reaction, and specific activity information of a catalyst in each reaction unit where the reforming reaction and the combustion reaction occur in the digital twin model 130. The simulation module 122 can also adjust the opening ratio of a feed flow control valve based on the collected information as an optimized scenario. For example, when the x data value is greater than the y data value, the opening ratio can be adjusted by z.

[0041] The feedback loop 123 may realize a feedback loop between the digital twin model 130 and the physical plant 110 to adjust the physical plant 110 based on the simulation results of the simulation module 122.

[0042] The feedback loop 123 can adjust at least one of the pipes 111 , the valves 112 , the pumps 113 , and the sensors 114 included in the physical plant 110 in real time based on the simulation results of the simulation module 122 .

[0043] The performance monitoring module 124 may monitor the performance of the physical plant 110 to track the effectiveness of the optimization process.

[0044] The performance monitoring module 124 can continuously monitor the performance of the physical plant 110 and the digital twin model 130 to determine whether the performance of the physical plant 110 and the digital twin model 130 meet pre-set goals.

[0045] The update module 125 can be continually updated based on the real-time data of the physical plant 110 to ensure validity.

[0046] The feedback module 126 can adjust the physical plant 110 in response to the optimized settings identified by the digital twin model 130.

[0047] Digital twin model 130 may be a model for physical plant 110. Digital twin model 130 may correspond to physical plant 110. Digital twin model 130 may include, for example, pipes 131, valves 132, pumps 133, and sensors 134.

[0048] As described above, optimizing the flow of gases such as ammonia can have the effect of improving the efficiency of the entire process, such as maximizing ammonia throughput, minimizing energy consumption, and identifying optimal operating conditions.

[0049] As previously mentioned, optimizing gas flow also has the effect of improving the environmental impact by preventing the release of harmful emissions.

[0050] Also, as described above, there is an effect of improving system reliability by preventing potential system errors or malfunctions in the physical plant.

[0051] In addition, as mentioned above, by providing real-time response to changing conditions through a feedback loop between the digital twin model and the physical plant, it has the effect of quickly taking control actions, adjustment actions, tuning actions, etc.

[0052] FIG. 2 is a diagram illustrating a relationship between a plant and a digital twin model according to one embodiment of the present disclosure.

[0053] Referring to FIG. 2, the relationship diagram 200 may include a dynamic load input 210, a digital twin output quantity control model 220, first and second blowers 231, 232, a digital twin flow equal distribution model 240, first information 250, first and second control valves 261, 262, a reformer 270, a fuel cell 280, and second information 290.

[0054] The dynamic load input 210, the first information 250, and the second information 290 may be input to the digital twin output quantity control model 220. The first information 250 may include, for example, temperature, pressure, flow rate, etc. The second information 290 may include, for example, voltage, current, etc.

[0055] The digital twin output quantity control model 220 can output output values ​​to the first and second blowers 231, 232.

[0056] The reforming reaction feed may be input to the first blower 231. The fuel and air for the combustion reaction may be input to the second blower 232.

[0057] The second information 290 may be input to the digital twin flow rate equal distribution model 240. The digital twin flow rate equal distribution model 240 may output output values ​​to the first and second control valves 261, 262. A reforming reaction feed may be input to the reformer 270 via the first control valve 261. Fuel and air for the combustion reaction may be input to the reformer 270 via the second control valve 262.

[0058] The reaction may occur via reformer 270. Electricity may be generated via fuel cell 280.

[0059] In the case of the optimized scenario described above with reference to FIG. 1, an input indicating the amount of energy required, for example, a dynamic load input 210, may be collected by the digital twin output amount control model 220. The input indicating the amount of energy required may include various parameters included in, for example, a graph indicating the amount of energy required when a vehicle is decelerating or accelerating, a graph indicating the change in the amount of power consumption by time of day of a distributed power generation system, etc. And, the power output of the fuel cell sensed in the physical plant may be collected by the digital twin output amount control model 220. The flow rate of the feed for the reforming reaction occurring in the reformer 270, for example, the reforming reaction feed, may be collected by the digital twin flow rate uniform distribution model 240. The flow rate of the feed for the combustion reaction, for example, the fuel and air for the combustion reaction, may be collected by the digital twin flow rate uniform distribution model 240. Specific activity information of the catalyst of each reaction unit where the reforming reaction and the combustion reaction occur may be collected by the digital twin flow rate uniform distribution model 240. The specific activity of the catalyst can be obtained by measuring the temperature, composition, and flow rate of the reforming reaction product and the combustion flue gas, and calculating the conversion rate and selectivity. Based on the collected information, the opening rate of the flow control valve for each feed can be adjusted. In this case, the higher the specific activity of the catalyst, the less feed can be injected.

[0060] FIG. 3 is a diagram for explaining a method according to an embodiment of the present disclosure.

[0061] Referring to FIG. 3, a method according to one embodiment of the present disclosure can optimize ammonia gas flow in an industrial plant.

[0062] A step of acquiring real-time data from a physical plant is performed (S100).

[0063] A step of simulating at least one scenario using a digital twin model for a physical plant is performed (S200).

[0064] Based on the simulation results, a step of identifying optimal operating conditions for the ammonia gas flow is performed (S300).

[0065] In relation to steps S200 and S300, for example, with reference to FIG. 1, the simulation module 122 may simulate at least one scenario using the digital twin model 130 and identify optimal operating conditions for the flow of ammonia gas based on the simulation results. Here, the operating conditions may include, for example, valve positions, pump speeds, and pipe characteristics. The simulation module 122 may collect, using the digital twin model 130, inputs indicating the amount of energy required in the system 100 and outputs of the physical plant 110, the flow rate of a feed for the reforming reaction, the flow rate of the feed for the combustion reaction, and specific activity information of the catalyst of each reaction unit where the reforming reaction and the combustion reaction occur. Then, the simulation module 122 may adjust the opening rate of the feed flow control valve based on the collected information.

[0066] A step of implementing a feedback loop between the digital twin model and the physical plant is performed to adjust the physical plant based on the simulation results (S400).

[0067] Regarding step S400, for example, with reference to FIG. 1, the feedback loop 123 can realize a feedback loop between the digital twin model 130 and the physical plant 110 to adjust the physical plant 110 based on the simulation results of the simulation module 122, and adjust at least one of the pipes 111, the valves 112, the pumps 113, and the sensors 114 included in the physical plant 110 in real time.

[0068] To track the effectiveness of the optimization process, a step is performed of monitoring the performance of the physical plant (S500).

[0069] Regarding step S500, for example, with reference to FIG. 1, the performance monitoring module 124 can continuously monitor the performance of the physical plant 110 and the digital twin model 130 to track the efficiency of the optimization process and determine whether the performance of the physical plant 110 and the digital twin model 130 meet pre-set targets.

[0070] Meanwhile, the disclosed embodiments may be realized 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 realized as a computer-readable storage medium.

[0071] The computer-readable storage medium includes any kind of storage medium in which instructions that can be decoded by a computer are stored, such as a Read Only Memory (ROM), a Random Access Memory (RAM), a magnetic tape, a magnetic disk, a flash memory, an optical data storage device, and the like.

[0072] As described above, the disclosed embodiments have been described with reference to the accompanying drawings. Those skilled in the art to which the present disclosure pertains should understand that the present disclosure can be implemented in forms 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 optimizing ammonia gas flow in an industrial plant, comprising: A physical plant; a digital twin model for the physical plant; and a data collection module for acquiring real-time data from the physical plant; a simulation module that uses the digital twin model to simulate at least one scenario and identifies optimal operating conditions for the ammonia gas flow based on simulation results; a feedback loop that implements a feedback loop between the digital twin model and the physical plant to adjust the physical plant based on the simulation results; a performance monitoring module that monitors the performance of the physical plant to track the efficiency of the optimization process.

2. The physical plant comprises: The system of claim 1 including pipes, valves, pumps, and sensors.

3. The data collection module includes: The system of claim 1 , wherein the real-time data collected comprises at least one of temperature, pressure, flow rate, pump speed, pipe characteristics, and valve position from sensors.

4. The simulation module includes: The system of claim 3 , wherein the energy consumption of the system is minimized and the throughput handled by the system is maximized by adjusting operating conditions including at least one of the valve position, the pump speed, and the pipe characteristics.

5. The feedback loop comprises: The system of claim 1 , further comprising: adjusting at least one of pipes, valves, pumps, and sensors included in the physical plant in real time based on the simulation results.

6. The performance monitoring module includes:

2. The system of claim 1, further comprising: continuously monitoring the performance of the physical plant and the digital twin model to determine whether the performance of the physical plant and the digital twin model meet pre-established goals.

7. The system of claim 1 , further comprising an update module that continuously updates based on the real-time data of the physical plant.

8. 10. The system of claim 1, further comprising a feedback module that adjusts the physical plant in response to optimized settings identified by the digital twin model.

9. The simulation module includes: collecting inputs indicative of the amount of energy required and outputs of the physical plant in the digital twin model; The flow rate of the feed for the reforming reaction, the flow rate of the feed for the combustion reaction, and the specific activity information of the catalyst of each reaction unit in which the reforming reaction and the combustion reaction occur are collected by the digital twin model; The system of claim 1 , further comprising: adjusting an opening ratio of a feed flow control valve based on the collected information as an optimized scenario.

10. 1. A method for optimizing ammonia gas flow in an industrial plant, comprising: acquiring real-time data from a physical plant; simulating at least one scenario using a digital twin model for the physical plant; identifying optimal operating conditions for the ammonia gas flow based on the simulation results; implementing a feedback loop between the digital twin model and the physical plant to adjust the physical plant based on the simulation results; and monitoring the performance of the physical plant to track the efficiency of the optimization process.

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