Ammonia adsorption tower drive control device and control method using the same

Abstract

To provide an artificial intelligence-based ammonia adsorption tower driving control device and a control method using the same, capable of contributing to environmental friendliness and producing clean energy by executing an abnormal situation processing process when an abnormal state occurs.SOLUTION: A control device 100 includes: a sensor unit 110 configured to measure an internal state of an adsorption tower; a memory configured to store one or more instructions; a processor configured to execute the one or more instructions stored in the memory; and a driving unit configured to drive the adsorption tower according to an adsorption cycle and a desorption cycle set on the basis of the internal state of the adsorption tower. Therein the processor is configured to: output an adsorption cycle and desorption cycle according to sensing data of the sensor unit using a trained artificial intelligence model; when the sensing data is within a preset optimal range, transmit a command to drive the adsorption tower according to the output adsorption cycle and desorption cycle to the driving unit; and if the sensing data exceeds the optimal range, transmit a command to execute an abnormal situation processing process according to a preset exception adsorption cycle and exception desorption cycle to the driving unit.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present disclosure relates to ammonia adsorption tower The present invention relates to a drive control device and a control method using the same. [Background technology]

[0002] Ammonia is one of the substances that causes bad odors and can be generated not only in livestock barns, but also in composting facilities that use chemical fertilizers and synthetic fibers, factories, and sewage treatment plants.

[0003] Traditionally, adsorption tower is driven to adsorption tower It adsorbs and removes ammonia from the air that is introduced into the adsorption tower When the removal efficiency of ammonia decreased, the ammonia was desorbed and controlled so as to maintain a state in which it could be adsorbed again.

[0004] In order to repeat the above process, it is very important to control the adsorption / desorption cycle so that the adsorption and desorption processes are carried out at the appropriate time. Conventionally, the vapor recovery method was used, in which VOCs adsorbed on an adsorbent were desorbed using vapor, and the desorbed vapor and VOCs were then recovered and reused using a heat exchanger, and the time when the adsorbent was desorbed was detected using a temperature sensor.

[0005] However, even if the desorption time is detected or an abnormality warning is issued when the desorption time is exceeded, if the operator does not take the necessary steps to resolve the abnormality in a timely manner, the adsorption efficiency will decrease.Even if a timely response is taken, the method of responding to the abnormality will differ depending on the operator's level of experience, which can result in the problem of the adsorption efficiency not being maintained at a constant level.

[0006] Furthermore, even if an artificial intelligence model is used, the artificial intelligence model that outputs the optimal adsorption / desorption period in accordance with the normal state may repeatedly output incorrect predicted values ​​in accordance with the abnormal state, resulting in erroneous learning, which may also have an inaccurate effect on the prediction of the adsorption / desorption period in the normal state. Summary of the Invention [Problem to be solved by the invention]

[0007] According to one aspect of the present disclosure, when an abnormal condition occurs, an artificial intelligence-based ammonia process is performed along an exceptional adsorption period and an exceptional desorption period, regardless of a predicted adsorption / desorption period, thereby enabling the production of environmentally friendly and clean energy. adsorption tower The present invention provides a drive control device and a control method using the same.

[0008] Ammonia according to one embodiment of the present disclosure adsorption tower The drive control device includes: adsorption tower a sensor unit for measuring a condition within the device; a memory for storing one or more instructions; a processor for executing the one or more instructions stored in the memory; adsorption tower According to the adsorption and desorption cycles set based on the state of adsorption tower and a drive unit that drives the sensor unit, and the processor uses a trained artificial intelligence model to output corresponding adsorption and desorption periods according to sensing data from the sensor unit, and when the sensing data is within a preset optimum range, drives the sensor unit according to the output adsorption and desorption periods. adsorption tower and if the sensing data exceeds the optimum range, a command to perform an abnormal situation handling process according to a preset exceptional adsorption period and an exceptional desorption period can be transmitted to the driving unit.

[0009] According to an embodiment, the exceptional adsorption period and the exceptional desorption period may be the shortest adsorption period and the longest desorption period within a period range that can be set by the driving unit.

[0010] According to one embodiment, when the processor transmits an instruction to the driver to perform the abnormal situation handling process, the instruction may include at least one parameter of a minimum adsorption flow rate and a maximum desorption flow rate at which the driver can operate, a minimum adsorption temperature and a maximum desorption temperature at which the driver can operate, and a minimum adsorption pressure and a maximum desorption pressure at which the driver can operate.

[0011] According to one embodiment, the processor may be configured to repeatedly self-diagnose at a preset period whether the sensing data measured by the sensor unit falls outside an optimal range or whether the patterns of previous and subsequent sensing data are different.

[0012] According to one embodiment, the abnormal situation handling process adsorption tower After driving the sensor, if the sensing data measured by the sensor unit is within the optimum range, the adsorption period and desorption period output from the trained artificial intelligence model are used to adsorption tower A command to drive the actuator can be transmitted to the actuator.

[0013] According to one embodiment, the processor performs an abnormal situation handling process. adsorption tower The previous adsorption cycle and the previous desorption cycle that were performed before driving the adsorption tower When the next adsorption cycle and the next desorption cycle for driving the driving unit are different, the driving unit can be configured to calculate reduction data of cost and time during driving using the next adsorption cycle and the next desorption cycle.

[0014] According to one embodiment, if the calculated cost and time reduction data does not exceed a preset threshold, the processor may transmit a command to the driver to maintain the previous adsorption cycle and the previous desorption cycle as they are.

[0015] According to one embodiment, the processor can remove abnormal state sensing data at each point in the process and transmit only normal state sensing data to the artificial intelligence model so that the artificial intelligence model can detect abnormal states.

[0016] According to one embodiment, the system may further include a user interface that provides an alarm to a user when the abnormal situation occurs and accepts input of information related to the abnormal situation handling process.

[0017] According to one embodiment, the sensor unit includes at least one sensor selected from the group consisting of a pressure sensor, a temperature sensor, a humidity sensor, a gas sensor, and an adsorbent sensor, and the processor, when a self-diagnosis detects an error in the at least one sensor or a communication error between the at least one sensor and the processor, adsorption tower A command to stop driving the actuator can be transmitted to the actuator.

[0018] Ammonia according to one embodiment of the present disclosure adsorption tower The drive control method is performed by a computing device including a memory storing one or more instructions and a processor for executing the one or more instructions stored in the memory. adsorption tower a step of collecting sensing data that measures the state within the catalyst; and a step of measuring the state within the catalyst along an adsorption period and a desorption period that are set based on the sensing data. adsorption tower and driving the adsorption tower The step of driving the sensor includes a step of outputting the corresponding adsorption period and desorption period according to the sensing data using a trained artificial intelligence model, and, if the sensing data is within a preset optimum range, driving the sensor according to the output adsorption period and desorption period. adsorption tower and, if the sensing data exceeds the optimum range, transmitting a command to the driving unit to perform an abnormal situation handling process according to a preset exception adsorption period and an exception desorption period. [Effects of the Invention]

[0019] According to one embodiment, the present disclosure provides an abnormal situation handling process that, when an abnormal condition occurs, is performed independently of the predicted adsorption / desorption period. adsorption tower This allows the internal conditions to be quickly restored to normal, thereby maximizing costs and adsorption efficiency.

[0020] In addition, the present disclosure provides a method for predicting the adsorption and desorption cycles output by the artificial intelligence model. adsorption towerBy verifying the driving conditions before driving the device and changing the driving conditions according to the cost incurred when changing the adsorption / desorption cycle and the improved operating performance efficiency, the effect of maximizing the adsorption efficiency by saving the cost and time involved in changing the driving conditions is achieved. [Brief explanation of the drawings]

[0021] [Figure 1] FIG. 1 shows a simplified conceptual diagram of an adsorption tower according to one embodiment of the present disclosure. [Figure 2] FIG. 2 is a block diagram showing a simplified configuration of an ammonia adsorption tower drive control device according to an embodiment of the present disclosure. [Figure 3] FIG. 3 shows a flowchart of an ammonia adsorption tower drive control method according to one embodiment of the present disclosure. [Figure 4] FIG. 4 shows a specific flowchart of an ammonia adsorption tower drive control method according to one embodiment of the present disclosure. [Figure 5] FIG. 5 shows a flowchart of the ammonia adsorption tower drive control method according to one embodiment of the present disclosure when an abnormal state occurs. [Figure 6] FIG. 6 illustrates a computing device according to one embodiment of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[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 elements 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 terms "unit, module, component, block" used in this specification may be embodied in software or hardware, and depending on the embodiment, multiple "units, modules, components, blocks" may be embodied as one component, or one "unit, module, component, block" may include multiple components.

[0024] Throughout this specification, when a part is "coupled" to another part, it means not only a direct connection but also an indirect connection, including a connection 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 it may further include other components.

[0026] Throughout this specification, a member being "on" another member includes 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 to distinguish one component from another, 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 the steps, and the steps may be performed in a manner other than 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 various devices capable of performing computations and providing results 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 take 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] The functions related to artificial intelligence according to the present disclosure are operated by a processor and a memory. The processor may be configured with one or more processors. Here, the one or more processors may be general-purpose processors such as a CPU, AP, or DSP (Digital Signal Processor), dedicated graphics processors such as a GPU or VPU (Vision Processing Unit), or dedicated artificial intelligence processors such as an NPU. The one or more processors control the processing of input data according to predefined operating rules or artificial intelligence models stored in memory. Furthermore, if the one or more processors are dedicated artificial intelligence processors, the dedicated artificial intelligence processors may be designed with a hardware structure specialized for processing a specific artificial intelligence model.

[0035] The predefined behavioral rules or artificial intelligence models are characterized by being created through learning. Here, "created through learning" means that a basic artificial intelligence model is trained using a large amount of training data by a learning algorithm, thereby creating predefined behavioral rules or artificial intelligence models configured to achieve desired characteristics (or objectives). This learning may be performed on the device itself on which the artificial intelligence according to the present disclosure is executed, or may be performed by a separate server and / or system. Examples of learning algorithms include, but are not limited to, supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.

[0036] An artificial intelligence model may be composed of multiple neural network layers. Each of the multiple neural network layers has multiple weights, and neural network operations are performed through operations between the operation results of previous layers and the multiple weights. The multiple weights of the multiple neural network layers may be optimized based on the learning results of the artificial intelligence model. For example, the multiple weights may be updated so that the loss value or cost value obtained from the artificial intelligence model during the learning process is reduced or minimized. The artificial neural network may include a deep neural network (DNN), such as, but not limited to, a convolutional neural network (CNN), a deep neural network (DNN), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), or a deep Q-network.

[0037] According to an exemplary embodiment of the present disclosure, a processor may embody artificial intelligence. Artificial intelligence refers to a machine learning method based on an artificial neural network that imitates human biological neurons and allows a machine to learn. Artificial intelligence methodologies can be classified into supervised learning, in which input data and output data are provided together as training data according to a learning method, thereby determining the answer (output data) to a problem (input data); unsupervised learning, in which only input data is provided without output data, thereby determining the answer (output data) to a problem (input data); and reinforcement learning, in which a reward is provided from an external environment each time a certain action is taken in a current state, and learning is carried out in a direction to maximize the reward. Artificial intelligence methodologies can also be categorized by their architecture, which is the structure of the learning model. The architectures of widely used deep learning technologies can be categorized into convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and generative adversarial networks (GANs).

[0038] The present device and system may include an artificial intelligence model. The artificial intelligence model may be a single artificial intelligence model or may be embodied as multiple artificial intelligence models. The artificial intelligence model may be composed of a neural network (or artificial neural network) and may include a statistical learning algorithm that mimics biological neurons in machine learning and cognitive science. A neural network may refer to a general model in which artificial neurons (nodes) formed through synaptic connections change the strength of synaptic connections through learning, thereby having problem-solving capabilities. Neurons in a neural network may include a combination of weights or biases. A neural network may include one or more layers composed of one or more neurons or nodes. For example, a device may include an input layer, a hidden layer, and an output layer. The neural network constituting the device can infer a desired result (output) from an arbitrary input (input) by changing the neuron weights through learning.

[0039] The processor may generate a neural network, train or learn the neural network, perform operations based on received input data, generate an information signal based on the results of the operations, or retrain the neural network. Neural network models may include, but are not limited to, various types of models, such as convolution neural networks (CNNs) such as GoogleNet, AlexNet, and VGG Network, region with convolution neural networks (R-CNNs), region proposal networks (RPNs), recurrent neural networks (RNNs), stacking-based deep neural networks (S-DNNs), state-space dynamic neural networks (S-SDNNs), deconvolution networks, deep belief networks (DBNs), restricted Boltzmann machines (RBMs), fully convolutional networks, long short-term memory (LSTM) networks, and classification networks. The processor may include one or more processors for performing operations based on the neural network models. For example, the neural network may include a deep neural network.

[0040] Neural networks include CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), perceptron, multilayer perceptron, FF (Feed Forward), RBF (Radial Basis Network), DFF (Deep Feed Forward), LSTM (Long Short Term Memory), GRU (Gated Recurrent Unit), AE (Auto Encoder), VAE (Variational Auto Encoder), DAE (Denoising Auto Encoder), SAE (Sparse Auto Encoder), MC (Markov Chain), HN (Hopfield Network), BM (Boltzmann Machine), RBM (Restricted Boltzmann Machine), DBN (Deep Belief Network), DCN (Deep Convolutional Network), DN (Deconvolutional Network), DCIGN (Deep Convolutional Inverse Graphics Network), and GAN (Generative Adversarial Network). A person of ordinary skill in the art will understand that the neural network may include, but is not limited to, any neural network, such as a LSM (Liquid State Machine), an ELM (Extreme Learning Machine), an ESN (Echo State Network), a DRN (Deep Residual Network), a DNC (Differentiable Neural Computer), an NTM (Neural Turning Machine), a CN (Capsule Network), a KN (Kohonen Network), and an AN (Attention Network).

[0041] According to an exemplary embodiment of the present disclosure, the processor may support a variety of neural networks, including Convolution Neural Networks (CNNs) such as GoogleNet, AlexNet, and VGGNetwork, Region with Convolution Neural Network (R-CNN), Region Proposal Network (RPN), Recurrent Neural Network (RNN), Stacking-based deep Neural Network (S-DNN), State-Space Dynamic Neural Network (S-SDNN), Deconvolution Network, Deep Belief Network (DBN), Restricted Boltzmann Machine (RBM), Fully Convolutional Network, Long Short-Term Memory (LSTM) Network, Classification Network, Generative Modeling, eXplainable AI, Continual AI, Representation Learning, AI for Material Design, BERT, SP-BERT, MRC / QA for Natural Language Processing, Text Analysis, Dialog System, GPT-3, GPT-4, Visual Analytics, Visual Understanding, Video Synthesis for Vision Processing, Anomaly Detection, Prediction, and Time-Series for ResNet data intelligence. Various artificial intelligence structures and algorithms may be used, including but not limited to forecasting, optimization, recommendation, data creation, etc. Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings.

[0042] FIG. 1 is a block diagram of a system according to one embodiment of the present disclosure. adsorption tower 10, and the above-mentioned AI-based ammonia adsorption towerThe drive control device and the control method using the same will now be described in detail.

[0043] As shown in Figure 1, adsorption tower The device 10 receives air (Feed) containing ammonia, volatile organic compounds (VOCs), etc., and applies vapor recovery methods such as recompression, condensation, and activated carbon adsorption between T1 and T4 to discharge air (Product) from which ammonia, volatile organic compounds (VOCs), etc. have been removed.

[0044] adsorption tower The sensor 10 can be divided into areas T1, T2, T3, and T4, from the area close to the intake port where the feed is input to the area close to the outlet where the product is discharged, and a sensor unit 110 (see FIG. 2) can be provided in each of the divided areas. adsorption tower 10. A pressure sensor for measuring pressure within the adsorption tower 10. A temperature sensor for measuring the temperature within adsorption tower 10. A humidity sensor for measuring humidity within the adsorption tower The system may include at least one of a gas sensor that measures the gas composition within 10 to test the purity of the extracted hydrogen, and a sorbent sensor.

[0045] Using the sensor unit 110, it is possible to check whether the pressure or temperature is maintained within a certain range to maintain adsorption efficiency, and the result value regarding hydrogen extraction efficiency can be calculated using humidity or hydrogen purity.

[0046] Furthermore, the adsorbent sensor of the sensor unit 110 is adsorption tower The condition of the adsorbent in the system can be measured, and information can be obtained that can be used to evaluate the life and performance of the adsorbent and predict when to replace it.

[0047] As shown in Figure 1, the temperature rises as the adsorption becomes more active, but as the adsorption efficiency decreases and the time to replace the adsorbent approaches, the temperature rise decreases. Therefore, the change in the state of the adsorbent can be determined by the amount of change in temperature.

[0048] In this case, the contact time with the feed becomes slower from T1 to T4, and the time to replace the adsorbent occurs later. It can be seen that the time to replace the adsorbent from T1 to T4 does not occur all at once.

[0049] In addition, if an abnormal condition occurs, even if the time for replacing the adsorbent has not yet arrived, adsorption tower The range of temperature change within 10 decreases, which may lead to the mistaken belief that it is time to replace the adsorbent.

[0050] Therefore, the artificial intelligence-based ammonia adsorption tower As shown in FIG. 2, the drive control device 100 adsorption tower a sensor unit 110 for measuring conditions within the system 10; a memory 16 for storing one or more instructions; a processor 14 for executing the one or more instructions stored in the memory 16; adsorption tower According to the adsorption and desorption cycles set based on the state in 10, adsorption tower 10 and a drive unit 130 for driving the drive unit 10.

[0051] The sensor unit 110 includes: adsorption tower 10, can be placed at T1 to T4, respectively.

[0052] In one embodiment, adsorption tower The interior of the apparatus 10 can be separated and partitioned into a plurality of beds, and the sensor unit 110 can be provided in each of the separated and partitioned beds.

[0053] In another embodiment, a plurality of adsorption tower 10 are connected to each adsorption tower 10 may each be provided with a sensor unit 110.

[0054] In other words, one adsorption tower 10 divided into multiple beds or multiple adsorption tower At least one space separated, such as 10 adsorption tower A sensor unit 110 can be provided for each of the separated spaces 10 .

[0055] The processor 14 and the memory 16 may be located together or individually in one or more computing devices 120. The computing devices 120 may be used to: adsorption tower 10 efficiency is maintained above a certain level. adsorption tower It can control 10 drives.

[0056] Artificial intelligence model 160 may be co-located on the computing device 120 or may be linked via a separate server or cloud.

[0057] In addition, an artificial intelligence-based ammonia adsorption tower The drive control device 100 adsorption tower A user interface 140 may further be included to receive user input for operation of the device 10 .

[0058] The user interface 140 can provide an alarm to the user when an abnormal condition occurs and can accept input of information related to the abnormal situation handling process.

[0059] In addition, an artificial intelligence-based ammonia adsorption tower The drive control device 100 adsorption tower Provides 10 driving alarms or real-time adsorption tower The device may further include a display unit 150 that monitors the operating state of the device 10 and displays the monitoring results.

[0060] In one embodiment, the processor 14 uses the trained artificial intelligence model 160 to output corresponding adsorption and desorption periods according to the sensing data of the sensor unit 110, and if the sensing data is within a preset optimum range, performs the following operation according to the output adsorption and desorption periods. adsorption towerIf the sensing data exceeds the optimum range, a command to perform an abnormal situation handling process according to a preset exceptional adsorption period and exceptional desorption period can be transmitted to the driving unit 130.

[0061] AI-based ammonia adsorption tower The drive control device 100 identifies an abnormal state based on the data collected by the sensor unit 110 and performs the following at each point in the adsorption process: adsorption tower By using an artificial intelligence model 160 that has learned the normal conditions of the pressure, temperature, humidity, concentration, etc. of the gas inside 10, when an abnormal condition occurs, it is possible to detect the abnormal condition in real time.

[0062] AI-based ammonia adsorption tower The processor 14 of the drive control device 100 repeatedly performs self-diagnosis at a preset interval to determine whether the sensing data measured by the sensor unit 110 is outside the optimal range or whether the patterns of the previous sensing data and the subsequent sensing data are different, and can detect abnormal conditions in real time when they occur using the artificial intelligence model 160.

[0063] Furthermore, when the processor 14 proceeds with the self-diagnosis, adsorption tower 10, and also detects the occurrence of a sensor error in at least one sensor of the sensor unit 110 or a communication error between the sensor unit 110 and the processor 14, and adsorption tower The driver 130 may receive a command to stop driving the motor 10 .

[0064] In one embodiment, when a normal state is identified, the processor 14 may perform the adsorption process at optimal efficiency using the adsorption period and desorption period output by the artificial intelligence model 160. In this case, optimal efficiency refers to a state in which the adsorption efficiency can be maintained at a specific level or above while reducing costs by delaying the replacement of the adsorbent as much as possible.

[0065] In one embodiment, when an abnormal state is identified, the processor 14 does not perform the adsorption process in the adsorption and desorption cycles output by the artificial intelligence model 160, but instead executes a preset abnormal situation processing process to select the adsorption and desorption cycle that will allow the quickest recovery to a normal state. adsorption tower It can command 10 drives.

[0066] Here, the adsorption / desorption period that returns to the normal state most quickly is a preset exceptional adsorption period and exceptional desorption period, and the exceptional adsorption period and exceptional desorption period may be the shortest adsorption period and the longest desorption period within the period range that can be set by the driving unit 130.

[0067] Alternatively, when the processor 14 transmits an instruction to the driver 130 to perform the abnormal situation treatment process, the instruction may include at least one of parameters of the minimum adsorption flow rate and maximum desorption flow rate, the minimum adsorption temperature and maximum desorption temperature, or the minimum adsorption pressure and maximum desorption pressure that the driver 130 can drive.

[0068] Specifically, the processor 14 can transmit exception parameters corresponding to abnormal situations other than the exception adsorption cycle and the exception desorption cycle to the driver 130. The minimum adsorption flow rate and maximum desorption flow rate at which the driver 130 can operate can be transmitted, and the minimum adsorption flow rate that can be handled while maintaining optimal adsorption efficiency before the adsorbent reaches the replacement cycle can be measured or calculated, and the maximum desorption flow rate that can be handled while maintaining optimal desorption efficiency before the adsorbent reaches the replacement cycle can be measured or calculated.

[0069] Alternatively, the processor 14 may transmit the minimum adsorption temperature and maximum desorption temperature or minimum adsorption pressure and maximum desorption pressure at which the drive unit 130 can be driven, and measure or calculate the minimum adsorption temperature or pressure that occurs when the adsorbent maintains the optimum adsorption efficiency before reaching the replacement cycle, and measure or calculate the maximum desorption temperature or pressure that occurs when the adsorbent maintains the optimum desorption efficiency before reaching the replacement cycle.

[0070] At this time, the adsorption period and desorption period output by the artificial intelligence model 160 can be separately saved so that they can be used when the normal state is restored later.

[0071] In one embodiment, after identifying an abnormal condition, the abnormal situation handling process adsorption tower After the operation of the sensor 110, if the sensing data measured by the sensor unit 110 is again within the optimum range, the sensor 110 calculates the adsorption period and desorption period using the adsorption period and desorption period output from the trained artificial intelligence model 160. adsorption tower It can drive 10.

[0072] At this time, the artificial intelligence model 160 can be operated again to output the adsorption period and desorption period, or the adsorption period and desorption period output by the artificial intelligence model 160 that were previously stored separately can be called up and used.

[0073] Therefore, AI-based ammonia adsorption tower The drive control device 100 distinguishes between normal and abnormal states in advance and performs a separate adsorption process accordingly. adsorption tower It can control 10 drives.

[0074] On the other hand, AI-based ammonia adsorption tower The processor 14 of the drive control device 100 executes the abnormal situation processing process. adsorption tower The previous adsorption cycle and the previous desorption cycle performed before driving 10 and after the abnormal situation treatment process is completed adsorption tower When the next adsorption cycle and the next desorption cycle for driving the driving unit are different, the next adsorption cycle and the next desorption cycle can be used to calculate the cost and time reduction data when the driving unit is driven.

[0075] Specifically, before identifying an abnormal state, adsorption towerIf there is an adsorption / desorption period (P1) that was driving 10, an exceptional adsorption / desorption period (P2), and an exceptional adsorption / desorption period (P2) that was output again by the artificial intelligence model 160 after returning to a normal state, the processor 14 does not immediately perform the adsorption process using P2, but can perform the adsorption process after finally verifying the adsorption processes using P1 and P2.

[0076] For example, from P1 to P2 adsorption tower When changing the operating conditions of 10, the cost incurred and the improved operating efficiency are calculated and compared, and then the adsorption / desorption cycle can be changed.

[0077] In other words, the calculated planned cost and efficiency for changing the adsorption / desorption cycle are compared with the cost and efficiency of the current adsorption / desorption cycle, and the adsorption / desorption cycle is changed only if there is an effect of reducing costs and increasing efficiency equal to or greater than a preset threshold. adsorption tower 10 can be controlled.

[0078] Considering the losses caused by frequent changes in adsorption / desorption cycles and the process delays during the cycle change process, adsorption tower Reflecting 10 driving situations adsorption tower 10 control is possible.

[0079] Therefore, AI-based ammonia adsorption tower If the calculated cost and time reduction data does not exceed a preset threshold, the processor 14 of the drive control device 100 can transmit a command to the drive unit 130 to maintain the previous adsorption cycle and the previous desorption cycle as they are.

[0080] AI-based ammonia adsorption tower The processor 14 of the drive control device 100 adsorption tower The driving data of the 10 can be transmitted as learning data to the artificial intelligence model 160. In order for the artificial intelligence model 160 to detect abnormal states, sensing data of abnormal states can be removed at each point in the process, and only sensing data of normal states can be transmitted to the artificial intelligence model.

[0081] On the other hand, as shown in Figure 3, AI-based ammonia adsorption tower The control method using the drive control device 100 is as follows: adsorption tower A step (S310) of collecting sensing data measuring the state inside the 10, and adsorption tower 10 (S320).

[0082] The aforementioned adsorption tower The step of driving the device 10 includes a step of outputting the corresponding adsorption period and desorption period according to the sensing data using the trained artificial intelligence model 160, and if the sensing data is within a preset optimum range, driving the device 10 according to the output adsorption period and desorption period. adsorption tower The method may further include transmitting a command to the driving unit 130 to drive the sensor 10, and, if the sensing data exceeds the optimal range, transmitting a command to the driving unit 130 to perform an abnormal situation handling process according to a preset exception adsorption period and an exception desorption period.

[0083] Specifically, as shown in FIG. 4, in the data collection step (S410), the initial adsorption tower The sensing data can be collected in 10. Then, in a data preprocessing step (S420), the sensing data can be subjected to data preprocessing such as data normalization, missing value processing, and removal of outliers measured in abnormal conditions.

[0084] In the artificial intelligence model learning step (S430), the artificial intelligence model 160 is trained using the preprocessed data to obtain the optimal adsorption tower A model can be generated that predicts the operating conditions of 10. Using a machine learning or deep learning algorithm, the system can be trained to output an optimal adsorption / desorption cycle corresponding to the sensing data, and in particular, the system can be trained to output an optimal adsorption / desorption cycle taking into consideration the adsorbent replacement cycle and cost, optimal adsorption efficiency, the time required for each process to maintain the optimal adsorption efficiency, and gas purity.

[0085] When the learning is completed, in the adsorption / desorption period output step (S440) using the learned artificial intelligence model 160, the current adsorption tower The optimal adsorption / desorption period corresponding to the state within 10 can be output.

[0086] The adsorption / desorption process may not be performed immediately in the predicted adsorption / desorption cycle, but an abnormal state detection step (S450) may be performed, and if no abnormal state is detected, a drive condition verification step (S460) may be performed.

[0087] In the abnormal state detection step (S450), the current amount of adsorbent and adsorption tower Depending on the progress of the process within 10, it can detect deviations from the expected temperature, humidity, or pressure range, determine the difference between the predicted value and the current measured value, and if the difference is greater than or equal to a threshold, identify it as an abnormal state and provide an alarm on the display unit 150.

[0088] The difference between the predicted value and the current measured value is determined, and if the difference is less than the threshold and does not fall outside the normal state range, a drive condition verification step (S460) can determine whether to maintain or change the existing adsorption / desorption cycle based on the amount of adsorbent, amount of energy, application time for each step, etc. required for gas production.

[0089] Finally, with the completion of the verification of the driving conditions, the adsorption / desorption cycle was determined. adsorption tower Ten driving steps (S470) can be performed.

[0090] Alternatively, as shown in Fig. 5, when an abnormal state is identified in the abnormal state detection step (S510), an abnormal situation processing process execution step (S520) can be carried out in accordance with the exception adsorption / desorption cycle. The abnormal state detection step (S510) is the same step as S410.

[0091] In the abnormal situation processing process execution step (S520), the adsorption / desorption process is performed according to the preset exceptional adsorption period and exceptional desorption period, and the process is completed as quickly as possible. adsorption tower 10 can be restored to normal.

[0092] Thereafter, in a step of detecting return to normal state (S530), a self-diagnosis is performed by performing an adsorption / desorption process according to the exceptional adsorption cycle and the exceptional desorption cycle, and when the sensing data measured by the sensor unit 110 falls within the normal state range, a return to the normal state is detected, and a step of outputting an adsorption / desorption cycle using the trained artificial intelligence model 160 (S540) can be performed. The step of outputting an adsorption / desorption cycle using the trained artificial intelligence model 160 (S540) is the same step as S440. After S540, S450, S460, and S470 can be performed successively.

[0093] In addition, AI-based ammonia adsorption tower The processor 14 of the drive control device 100 performs the following operations according to the output adsorption period and desorption period. adsorption tower After driving the sensor unit 110, if the actual process result measured by the sensor unit 110 exceeds a preset range of the predicted process result, a command to perform an abnormal situation handling process according to the exceptional adsorption period and the exceptional desorption period can be transmitted to the driving unit 130.

[0094] Or AI-based ammonia adsorption tower The processor 14 of the drive control device 100 performs the following operations according to the output adsorption period and desorption period. adsorption tower After the operation of the sensor unit 110, if the actual process result measured by the sensor unit 110 exceeds a preset range of the predicted process result, a re-learning command can be transmitted to the artificial intelligence model 160.

[0095] If the sensing data of the sensor unit 110 is within the normal range but the adsorption efficiency is not as expected, the abnormal situation processing process is activated. adsorption towerThe state within 10 can be reset once or retrained to eliminate the possibility of mis-training of the artificial intelligence model 160.

[0096] In another embodiment, an artificial intelligence-based ammonia adsorption tower The processor 14 of the drive control device 100 can determine whether the abnormal condition is temporary or permanent and apply different abnormal situation handling processes depending on the abnormal condition.

[0097] In the case of a temporary erroneous measurement by the sensor unit 110 or a communication network failure due to an external factor, since this is a temporary abnormal state, the adsorption / desorption process can be performed using a preset exceptional adsorption / desorption cycle.

[0098] If the abnormal state is due to a failure of the sensor unit 110 or a communication error between at least one sensor unit 110 and the processor 14 due to a failure of the communication unit, the abnormal state is a permanent abnormal state that will not be resolved unless repaired externally. adsorption tower 10 driving can be stopped.

[0099] Alternatively, in the case of a permanent abnormal state, the average value of the output adsorption / desorption cycles stored in the memory 16 can be calculated, and the adsorption / desorption process can be performed according to the calculated adsorption / desorption cycle. adsorption tower It can drive 10.

[0100] In another embodiment, an abnormal condition is an exceptional pattern that deviates from a normal condition, and parameters measured in the normal condition, e.g., the artificial intelligence model 160 adsorption tower The temperature changes within 10 can be patterned and learned, and if the pattern changes, an abnormal state can be detected.

[0101] 1, if an abnormal state exhibits a temperature pattern different from that in a normal state, the AI ​​model 160 can detect the difference from the learned pattern and proceed with the abnormal situation processing process. In this case, the pattern can include a temperature pattern, a pressure pattern, a humidity pattern, a gas concentration pattern, etc.

[0102] This means that adsorption tower The desorption process is carried out in a timely manner so that the efficiency of 10 can be maintained above a certain level and the adsorbent can be used continuously, and stable operation is possible not only under normal conditions but also under abnormal conditions. adsorption tower 10 drives are possible.

[0103] In addition, through simulations before and after controlling the adsorption / desorption cycle, cost and adsorption efficiency were taken into consideration. adsorption tower By driving 10, cost savings and optimal adsorption efficiency can be maintained for a long period of time.

[0104] Meanwhile, as shown in FIG. 6, the AI-based ammonia adsorption tower The drive control device 100 may be embodied in the form of a storage medium storing computer-executable instructions. The instructions may be stored in the form of program code, which, 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.

[0105] AI-based ammonia according to the present disclosure adsorption tower The drive controller may correspond to a computing device 12. The computing device 12 may include at least one processor 14, a computer-readable storage medium 16 containing a program 20, and a communication bus 18. The computing device 12 may also include one or more input / output interfaces 22 that provide an interface for input / output devices 24, and one or more network communication interfaces 26.

[0106] The computing device 120 of the present disclosure may correspond to the computing device 12. The user interface 140 and display unit 150 of the present disclosure may correspond to one or more input / output interfaces 22 that provide an interface for the input / output device 24. Therefore, the AI-based ammonia according to the present disclosure may adsorption tower The components of the drive control device 100 may all be included in the single computing device 12 described above, or may each be embodied in a separate device.

[0107] The disclosed embodiments have been described above with reference to the accompanying drawings. Those skilled in the art will 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. [Explanation of symbols]

[0108] 100 ammonia adsorption tower Drive control device 110 Sensor unit 120 Computing Devices 14 processors 16 memory 130 Drive unit 140 User Interface 150 Display unit 160 Artificial Intelligence Models

Claims

1. A sensor unit including at least one sensor selected from the group consisting of a pressure sensor, a temperature sensor, a humidity sensor, a gas sensor, and an adsorbent sensor, and measuring the state inside the adsorption tower; a memory storing one or more instructions; a processor for executing the one or more instructions stored in the memory; a drive unit that drives the adsorption tower in accordance with an adsorption period and a desorption period that are set based on a state inside the adsorption tower, The processor: Using the trained artificial intelligence model, outputting corresponding adsorption and desorption periods in accordance with the sensing data of the sensor unit; If the sensing data is within a predetermined optimum range, a command to drive the adsorption tower according to the output adsorption period and desorption period is transmitted to the driving unit; If the sensing data exceeds the optimum range, a command is transmitted to the driving unit to perform an abnormal situation processing process according to a preset exceptional adsorption period and an exceptional desorption period. Ammonia adsorption tower drive control device.

2. the exceptional suction period and the exceptional desorption period are the shortest suction period and the longest desorption period within a period range that can be set in accordance with the sensing data of the driving unit, The ammonia adsorption tower drive control device according to claim 1.

3. When the processor transmits a command to the driving unit to perform the abnormal situation handling process, the command the minimum adsorption flow rate and the maximum desorption flow rate that can be driven in all possible cycle ranges of the driving unit; The minimum adsorption temperature and the maximum desorption temperature that can be driven in all possible cycle ranges of the driving unit, and a parameter including at least one of a minimum adsorption pressure and a maximum desorption pressure that can be driven in all cycle ranges that the driving unit can take, The ammonia adsorption tower drive control device according to claim 2.

4. The processor: The device is configured to repeatedly perform self-diagnosis at a preset period to determine whether the sensing data measured by the sensor unit falls outside an optimum range or whether the patterns of the previous sensing data and the subsequent sensing data differ. The ammonia adsorption tower drive control device according to claim 2.

5. The processor, After driving the adsorption tower in accordance with the abnormal situation processing process, if the sensing data measured by the sensor unit is within an optimum range, a command to drive the adsorption tower using the adsorption period and desorption period output from the trained artificial intelligence model is transmitted to the drive unit. The ammonia adsorption tower drive control device according to claim 4.

6. The processor: If the previous adsorption cycle and the previous desorption cycle performed before driving the adsorption tower according to the abnormal situation treatment process are different from the next adsorption cycle and the next desorption cycle in which the adsorption tower is driven after the abnormal situation treatment process is completed, The drive unit is configured to calculate reduction data of cost and time during operation using the next adsorption cycle and the next desorption cycle. The ammonia adsorption tower drive control device according to claim 5.

7. The processor: If the calculated cost and time reduction data do not exceed a preset threshold, a command to maintain the previous adsorption cycle and the previous desorption cycle is transmitted to the driving unit. The ammonia adsorption tower drive control device according to claim 6.

8. The processor removes abnormal state sensing data at each time point of the process and transmits only normal state sensing data to the artificial intelligence model so that the artificial intelligence model can detect the abnormal state. The ammonia adsorption tower drive control device according to claim 1.

9. a user interface configured to provide an alarm to a user when the abnormal condition occurs and to accept input of information regarding the abnormal situation handling process; The ammonia adsorption tower drive control device according to claim 8.

10. When an error in the at least one sensor is detected by self-diagnosis or a communication error between the at least one sensor and the processor is detected, the processor transmits a command to the drive unit to stop driving the adsorption tower. The ammonia adsorption tower drive control device according to claim 4.

11. 1. A method for controlling an ammonia adsorption tower, executed by a computing device including a memory storing one or more instructions and a processor executing the one or more instructions stored in the memory, the method comprising: collecting sensing data obtained by measuring a state inside the adsorption tower using at least one sensor selected from a pressure sensor, a temperature sensor, a humidity sensor, a gas sensor, and an adsorbent sensor; and driving the adsorption tower in accordance with an adsorption period and a desorption period that are set based on the sensing data, The step of driving the adsorption tower includes: outputting corresponding adsorption and desorption periods according to the sensing data using the trained artificial intelligence model; transmitting a command to a driving unit of the adsorption tower to drive the adsorption tower according to the output adsorption cycle and desorption cycle when the sensing data is within a predetermined optimum range; and if the sensing data exceeds the optimum range, transmitting a command to a driving unit of the adsorption tower to perform an abnormal situation processing process according to a preset exception adsorption period and an exception desorption period. Ammonia adsorption tower drive control method.

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