System and method for optimizing flow of ammonia

Abstract

The present disclosure relates to a system and method for optimizing the flow of ammonia in an industrial facility. A system according to the present disclosure includes: a physical facility; a digital twinborn model of the physical facility; the data collection module is used for acquiring real-time data from physical facilities; a simulation module that simulates the at least one protocol using a digital twinning model and identifies optimal operating conditions for the flow of ammonia based on the simulation results; the feedback loop is established between the digital twin model and the physical facility so as to adjust the physical facility based on the simulation result; and a performance monitoring module that monitors the performance of the physical facility to track the efficiency of the optimization process.

Description

Technical Field

[0001] The present disclosure relates to a system and method for optimizing the flow of ammonia. Background Art

[0002] In recent years, as various environmental issues have become a major concern throughout the world, there is a growing demand for introducing environmentally friendly alternative energy sources to replace traditional internal combustion engines not only in ordinary vehicles but also in heavy equipment such as excavators. Therefore, fuel cell systems using fuel cells (FC) as power sources have attracted much attention as environmentally friendly alternative energy sources.

[0003] To ensure smooth operation and high performance of the fuel cell system, the role of the Energy Management System (EMS) in properly determining how to distribute power demand among power sources is very important, which requires the EMS to operate stably and reliably.

[0004] In the presence of multiple pipelines, when the flow is concentrated in a specific pipeline, it is necessary to equalize the flow by automatically reducing the flow of that pipeline. Existing methods have difficulty accounting for the complex interactions between different components of the system. To address this problem, digital twins (DT) are being studied to provide accurate real-time simulation of the entire physical system. Summary of the invention

[0005] 1. Technical issues to be resolved

[0006] According to one aspect of the present disclosure, a system and method for controlling a flow rate in a specific pipe among which flows in a plurality of pipes are concentrated may be provided.

[0007] Furthermore, according to one aspect of the present disclosure, a system and method for optimizing the flow of ammonia using digital twin technology may be provided.

[0008] (II) Technical solution

[0009] A system according to an embodiment of the present disclosure may include: a physical facility; a digital twin model of the physical facility; a data collection module that acquires real-time data from the physical facility; a simulation module that simulates at least one scenario using the digital twin model and identifies optimal operating conditions for the flow of ammonia based on simulation results; a feedback loop that establishes a feedback loop between the digital twin model and the physical facility to adjust the physical facility based on the simulation results; and a performance monitoring module that monitors the performance of the physical facility to track the efficiency of the optimization process.

[0010] According to one embodiment, the physical facilities may include pipes, valves, pumps and sensors.

[0011] According to one embodiment, the data collection module may collect the real-time data from the sensor, the real-time data including at least one of temperature, pressure, flow, pump speed, pipeline characteristics, and valve position.

[0012] According to one embodiment, the simulation module may minimize energy consumption of the system and maximize throughput processed in the system by adjusting operating conditions including at least one of the valve position, the pump speed, and the pipeline characteristics.

[0013] According to one embodiment, the feedback loop may adjust at least one of a pipe, a valve, a pump, and a sensor included in the physical facility in real time based on the simulation result.

[0014] According to one embodiment, the performance monitoring module may continuously monitor the performance of the physical facility and the digital twin model to confirm whether the performance of the physical facility and the digital twin model meet preset goals.

[0015] According to one embodiment, an update module may be further included, and the update module is continuously updated based on the real-time data of the physical facility.

[0016] According to one embodiment, a feedback module may be further included, wherein the feedback module adjusts the physical facility in response to the optimization settings identified by the digital twin model.

[0017] According to one embodiment, the simulation module can collect inputs representing required energy and outputs of the physical facilities in the digital twin model, and the simulation module can collect flow rates of feeds for reforming reactions, flow rates of feeds for combustion reactions, and deactivation degree information of catalysts in each reaction unit where reforming reactions and combustion reactions occur in the digital twin model. As an optimization solution, the simulation module can adjust the opening ratio of the feed flow regulating valve based on the collected information.

[0018] According to one embodiment of the present disclosure, a method may include: executing a step of acquiring real-time data from a physical facility; executing a step of simulating at least one scenario using a digital twin model of the physical facility; executing a step of identifying optimal operating conditions for the flow of ammonia based on simulation results; executing a step of establishing a feedback loop between the digital twin model and the physical facility to adjust the physical facility based on the simulation results; and executing a step of monitoring the performance of the physical facility to track the efficiency of the optimization process.

[0019] (III) Beneficial effects

[0020] According to one embodiment of the present disclosure, the flow of ammonia or other gas is optimized, thereby having the effect of improving the efficiency of the entire process, such as maximizing the amount of ammonia processed, minimizing energy consumption, identifying optimal operating conditions, etc.

[0021] Furthermore, according to one embodiment of the present disclosure, the flow of gas is optimized, thereby having the effect of improving environmental impact, such as preventing the emission of harmful exhaust and the like.

[0022] Furthermore, according to an embodiment of the present disclosure, potential system errors or erroneous operations in a physical plant are prevented, thereby having the effect of improving system reliability.

[0023] Furthermore, according to one embodiment of the present disclosure, a real-time response is made to changing conditions through a feedback loop between the digital twin model and the physical facility, thereby having the effect of quickly executing control operations, regulation operations, adjustment operations, and the like. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 is a diagram for explaining a system according to an embodiment of the present disclosure.

[0025] Figure 2 is a relationship diagram between a facility and a digital twin model according to an embodiment of the present disclosure.

[0026] Figure 3 is a diagram for explaining a method according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0027] Hereinafter, the present disclosure will be described in detail with reference to the accompanying drawings. However, this is merely exemplary, and the present disclosure is not limited to the specific embodiments exemplarily described.

[0028] In the present disclosure, the same reference numerals refer to the same components. The present disclosure does not describe all the elements of each embodiment, and omits the general content in the technical field to which the present disclosure belongs or the content repeated between the embodiments. The terms "unit, module, component, block" used in the specification can be implemented by software or hardware. Depending on the embodiment, multiple "units, modules, components, blocks" can be implemented by a single component, or one "unit, module, component, block" can include multiple components.

[0029] Throughout the specification, when one part is referred to as being “connected” to another part, this includes not only the case of being directly connected but also the case of being indirectly connected including a connection through a wireless communication network.

[0030] In addition, when a portion is referred to as “comprising” a component, unless specifically described otherwise, it means that it can further include other components, rather than excluding other components.

[0031] Throughout the specification, when a member is described as being 'on' another member, this includes not only a case where one member is in contact with the other member but also a case where other members are present between the two members.

[0032] The terms “first”, “second”, etc. are used to distinguish one component from another component, and the components are not limited by the above terms.

[0033] Unless the context clearly indicates otherwise, an expression in the singular includes an expression in the plural.

[0034] The identification symbols of each step are used for convenience of description, and the identification symbols do not describe the order of the steps. Unless a specific order is clearly stated in the context, the steps can be performed in an order different from the specified order.

[0035] In this specification, "the device according to the present disclosure" includes all kinds of devices that can perform computing and provide results to users. For example, the device according to the present disclosure may include a computer, a server device, and a portable terminal, or any one of them.

[0036] Here, the computer may include, for example, a notebook computer equipped with a web browser, a desktop computer, a laptop computer, a tablet PC, a touch-screen tablet PC, etc.

[0037] The server device is a server that processes information by communicating with external devices, 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 network server, and the like.

[0038] Portable terminals are, for example, wireless communication devices that ensure portability and mobility, and may include all types of handheld 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, smart phones, and wearable devices such as watches, rings, bracelets, anklets, necklaces, glasses, contact lenses, or head-mounted devices (HMD).

[0039] Figure 1 is a diagram for explaining a system according to an embodiment of the present disclosure.

[0040] Reference Figure 1 The system 100 can optimize the flow of ammonia in an industrial plant. The system 100 can include a physical facility 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.

[0041] The physical facility 110 may include, for example, pipes 111 , valves 112 , pumps 113 , and sensors 114 .

[0042] The data collection module 121 may acquire real-time data from the physical facility 110. The data collection module 121 may acquire real-time data on various environments from the sensors 114 included in the physical facility 110. The real-time data may include, for example, at least one of temperature, pressure, flow, pump speed, pipeline characteristics, and valve position of the physical facility 110.

[0043] The simulation module 122 may simulate at least one scenario using the digital twin model 130. In addition, the simulation module 122 may identify optimal operating conditions for the flow of ammonia based on the simulation results.

[0044] The simulation module 122 can minimize the energy consumption of the system 100 by adjusting the operating conditions including at least one of the valve position, the pump speed, and the pipeline characteristics. In addition, the simulation module 122 can maximize the processing volume processed by the system 100.

[0045] The simulation module 122 can collect inputs representing the energy required by the system 100 and outputs of the physical facilities 110 in the digital twin model 130. In addition, the simulation module 122 can collect the flow rate of the feed (Feed) for the reforming reaction, the flow rate of the feed for the combustion reaction, and the deactivation degree information of the catalyst of each reaction unit where the reforming reaction and the combustion reaction occur in the digital twin model 130. In addition, as an optimization scenario, the simulation module 122 can adjust the opening ratio of the flow control valve of the feed based on the collected information. For example, when the x data value is greater than the y data value, the opening ratio can be adjusted to z.

[0046] The feedback loop 123 may establish a feedback loop between the digital twin model 130 and the physical facility 110 to adjust the physical facility 110 based on the simulation results of the simulation module 122 .

[0047] The feedback loop 123 may adjust at least one of the pipe 111 , the valve 112 , the pump 113 , and the sensor 114 included in the physical facility 110 in real time based on the simulation result of the simulation module 122 .

[0048] The performance monitoring module 124 may monitor the performance of the physical facility 110 to track the efficiency of the optimization process.

[0049] The performance monitoring module 124 may continuously monitor the performance of the physical facility 110 and the digital twin model 130 to confirm whether the performance of the physical facility 110 and the digital twin model 130 meet preset goals.

[0050] The update module 125 may continuously perform updates based on the real-time data of the physical facility 110 to ensure validity.

[0051] The feedback module 126 may adjust the physical facility 110 in response to the optimal settings identified by the digital twin model 130 .

[0052] The digital twin model 130 may be a model of the physical facility 110. The digital twin model 130 may correspond to the physical facility 110. The digital twin model 130 may include, for example, a pipe 131, a valve 132, a pump 133, and a sensor 134.

[0053] As described above, optimizing the flow of a gas such as ammonia has the effect of improving the efficiency of the entire process, such as maximizing the amount of ammonia processed, minimizing energy consumption, identifying optimal operating conditions, etc.

[0054] In addition, as described above, the flow of gas is optimized, thereby having the effect of improving environmental impact, such as preventing the emission of harmful emissions, etc.

[0055] In addition, as described above, potential system errors or erroneous operations in physical facilities are prevented, thereby having the effect of improving system reliability.

[0056] In addition, as described above, real-time responses to changing conditions are made through the feedback loop between the digital twin model and the physical facility, thereby having the effect of quickly performing control operations, regulation operations, adjustment operations, etc.

[0057] Figure 2 It is a relationship diagram between a facility-digital twin model (plant-Digital Twin Model) according to an embodiment of the present disclosure.

[0058] Reference Figure 2 , the relationship diagram 200 may include a dynamic load input (Dynamic Load input) 210, a digital twin output quantity control model 220, a first blower (Blower) 231, a second blower 232, a digital twin flow equalization distribution model 240, first information 250, a first control valve 261, a second control valve 262, a reformer 270, a fuel cell 280 and second information 290.

[0059] 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, etc. The second information 290 may include, for example, voltage, current, etc.

[0060] The digital twin output quantity control model 220 may output output values ​​to the first blower 231 and the second blower 232 .

[0061] Reforming reaction feed (Feed) may be input to the first blower 231 , and combustion reaction fuel (Fuel) and air (Air) may be input to the second blower 232 .

[0062] The second information 290 may be input to the digital twin flow equalization distribution model 240. The digital twin flow equalization distribution model 240 may output an output value to the first control valve 261 and the second control valve 262. The reforming reaction feed may be input to the reformer 270 through the first control valve 261. The combustion reaction fuel and air may be input to the reformer 270 through the second control valve 262.

[0063] The reaction may occur through the reformer 270. The fuel cell 280 may generate electricity.

[0064] For the above reference Figure 1 The optimization scenario described, the digital twin output control model 220 can collect inputs representing the required energy, such as the dynamic load input 210. The input representing the required energy can include various parameters included in, for example, a graph representing the energy required when the vehicle decelerates or accelerates, a graph representing the power consumption of the distributed power generation system over time, etc. In addition, the digital twin output control model 220 can collect the fuel cell power output sensed in the physical facility. The digital twin flow equalization distribution model 240 can collect the flow of the feed for the reforming reaction occurring in the reformer 270, such as the reforming reaction feed. The digital twin flow equalization distribution model 240 can collect the flow of the feed for the combustion reaction, such as the combustion reaction fuel and air. The digital twin flow equalization distribution model 240 can collect the deactivation degree information of the catalyst of each reaction unit where the reforming reaction and the combustion reaction occur. The deactivation degree of the catalyst can be obtained by measuring the temperature, composition and flow of the reforming reaction product and the combustion flue gas and calculating the conversion rate and selectivity. Based on the collected information, the opening of the flow control valve of each feed can be adjusted. At this time, the higher the degree of catalyst deactivation, the less feed can be injected.

[0065] Figure 3 is a diagram for explaining a method according to an embodiment of the present disclosure.

[0066] Reference Figure 3 , a method according to an embodiment of the present disclosure can optimize the flow of ammonia in an industrial facility.

[0067] The step of acquiring real-time data from a physical facility is performed (S100).

[0068] A step (S200) of simulating at least one scenario using a digital twin model of a physical facility is performed.

[0069] A step (S300) of identifying an optimal operating condition for the flow of ammonia based on the simulation result is performed.

[0070] Regarding steps S200 and S300, refer to Figure 1 Specifically, the simulation module 122 can use the digital twin model 130 to simulate at least one scenario, and can identify the optimal operating conditions for the flow of ammonia based on the simulation results. Here, the operating conditions may include, for example, valve position, pump speed, pipeline characteristics, etc. The simulation module 122 can collect inputs representing the energy required by the system 100, the output of the physical facility 110, the flow rate of the feed for the reforming reaction, the flow rate of the feed for the combustion reaction, and the deactivation degree information of the catalyst of each reaction unit where the reforming reaction and the combustion reaction occur in the digital twin model 130. In addition, the simulation module 122 can adjust the opening of the flow control valve of the feed based on the collected information.

[0071] A step ( S400 ) of establishing a feedback loop between the digital twin model and the physical facility to adjust the physical facility based on the simulation results is performed.

[0072] Regarding step S400, refer to Figure 1 By way of example, the feedback loop 123 establishes a feedback loop between the digital twin model 130 and the physical facility 110 to adjust the physical facility 110 based on the simulation results of the simulation module 122, and the feedback loop 123 can adjust at least one of the pipeline 111, valve 112, pump 113 and sensor 114 included in the physical facility 110 in real time.

[0073] A step (S500) of monitoring the performance of a physical facility to track the efficiency of an optimization process is performed.

[0074] Regarding step S500, refer to Figure 1 For example, the performance monitoring module 124 can continuously monitor the performance of the physical facility 110 and the digital twin model 130 to confirm whether the performance of the physical facility 110 and the digital twin model 130 meet preset goals to track the efficiency of the optimization process.

[0075] On the other hand, the disclosed embodiments may be implemented in the form of a recording medium storing instructions executable by a computer. The instructions may be stored in the form of program code, and when the instructions are executed by a processor, the operations of the disclosed embodiments may be performed by generating a program module. The recording medium may be implemented in the form of a computer-readable recording medium.

[0076] Computer-readable recording media include all types of recording media that store instructions that can be decoded by a computer, such as read-only memory (ROM), random access memory (RAM), magnetic tape, disk, flash memory, optical data storage device, etc.

[0077] The disclosed embodiments are described above with reference to the accompanying drawings. A person skilled in the art of the present disclosure will understand that the present disclosure may be implemented in a form different from the disclosed embodiments without changing the technical concept or essential features of the present disclosure. The disclosed embodiments are exemplary and should not be interpreted as limiting.

Claims

1. A system for optimizing the flow of ammonia in an industrial facility, the system comprising: Physical facilities; a digital twin of the physical facility; A data collection module, acquiring real-time data from the physical facility; a simulation module that simulates at least one scenario using the digital twin model and identifies optimal operating conditions for the flow of ammonia based on the simulation results; A feedback loop is established between the digital twin model and the physical facility to adjust the physical facility based on the simulation results; as well as A performance monitoring module monitors the performance of the physical facility to track the efficiency of the optimization process.

2. The system according to claim 1, wherein: The physical facilities include pipes, valves, pumps and sensors.

3. The system according to claim 1, wherein: The data collection module collects the real-time data from the sensor, the real-time data including at least one of temperature, pressure, flow, pump speed, pipeline characteristics, and valve position.

4. The system according to claim 3, wherein: The simulation module minimizes energy consumption of the system and maximizes throughput processed in the system by adjusting operating conditions including at least one of the valve position, the pump speed, and the pipeline characteristics.

5. The system according to claim 1, wherein: The feedback loop adjusts at least one of a pipe, a valve, a pump, and a sensor included in the physical facility in real time based on the simulation result.

6. The system according to claim 1, wherein: The performance monitoring module continuously monitors the performance of the physical facility and the digital twin model to confirm whether the performance of the physical facility and the digital twin model meet preset goals.

7. The system of claim 1, further comprising: An updating module is continuously updated based on the real-time data of the physical facility.

8. The system of claim 1, further comprising: A feedback module adjusts the physical facility in response to the optimization settings identified by the digital twin model.

9. The system according to claim 1, wherein: The simulation module collects inputs representing required energy and outputs of the physical facility in the digital twin model, The simulation module collects, in the digital twin model, information on the flow rate of feed for reforming reaction, the flow rate of feed for combustion reaction, and the degree of deactivation of catalysts in each reaction unit where reforming reaction and combustion reaction occur. As an optimization solution, the simulation module adjusts the opening of the flow control valve of the feed based on the collected information.

10. A method for optimizing the flow of ammonia in an industrial facility, the method comprising: Perform steps to obtain real-time data from physical facilities; performing the step of simulating at least one scenario using a digital twin of the physical facility; performing the step of identifying optimal operating conditions for the flow of ammonia based on the simulation results; performing the step of establishing a feedback loop between the digital twin model and the physical facility to adjust the physical facility based on the simulation results; as well as The step of monitoring the performance of the physical facility to track the efficiency of the optimization process is performed.

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