Adsorption tower for controlling liquid supply route to plurality of beds and operating control method thereof

Abstract

To provide: an adsorption tower for controlling a liquid supply route to a plurality of beds capable of contributing to environmental friendliness and producing clean energy; and an operating control method thereof.SOLUTION: An adsorption tower comprises: a plurality of beds therein; an inlet port connected to one side thereof; and an outlet port connected to the other side thereof. The adsorption tower further comprises: sensor units disposed inside the plurality of beds, respectively; a plurality of connection pipes which connect at least two or more of the inlet port or the outlet port and the plurality of beds; a plurality of valves connected to the plurality of connection pipes; a memory configured to store one or more instructions; and a processor configured to execute the one or more instructions stored in the memory.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The disclosure of the present application relates to an absorber drive control device and control method, and more specifically to an absorber that controls fluid supply paths to a plurality of beds in accordance with an adsorption value of each section, and a drive control method thereof. [Background technology]

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

[0003] Conventionally, an absorption tower is operated to adsorb and remove ammonia, and when the ammonia adsorption reaches a certain level or more and the removal efficiency of the absorption tower decreases, the ammonia is desorbed and the absorption tower is controlled so as to maintain a state in which the ammonia can be adsorbed again.

[0004] However, since the front section of the absorption tower adsorbs the fluid with the highest ammonia concentration and the rear section adsorbs the fluid with the lowest ammonia concentration, there was a large difference in adsorption efficiency or consumption rate of the adsorbent between the front and rear sections. In particular, the adsorbent in the front section was rapidly consumed and the adsorption / desorption cycle was shorter than that of the other sections, resulting in a short lifespan and replacement cycle. Summary of the Invention [Problem to be solved by the invention]

[0005] An object of the present disclosure is to provide an absorption tower and a drive control method thereof that control the fluid supply paths to multiple beds, thereby improving adsorption efficiency, reducing the operating costs of the entire absorption tower, and extending the lifespan of multiple beds by controlling the order in which fluids are supplied to differ depending on the adsorption values ​​of each section, thereby contributing to environmental friendliness and enabling the production of clean energy.

[0006] The problems to be solved by the present disclosure are not limited to those mentioned above, and other problems not mentioned will be clearly understood by those skilled in the art from the following description. [Means for solving the problem]

[0007] The absorption tower according to the present disclosure, which controls fluid supply paths to a plurality of beds, is an absorption tower including a plurality of beds arranged therein, an inlet connected to one side, and an outlet connected to the other side, and includes a sensor unit arranged in each of the plurality of beds, a plurality of connecting pipes connecting the inlet, the outlet, and at least two or more of the plurality of beds, a plurality of valves connected to the plurality of connecting pipes, a memory that stores one or more instructions, and a processor that executes the one or more instructions stored in the memory, and the processor can be configured to determine a fluid supply sequence to the plurality of beds according to the adsorption values ​​in the plurality of beds measured by the sensor unit, and to open and close the valves according to the determined fluid supply sequence to control the fluid supply paths.

[0008] The method for controlling an absorption tower for controlling fluid supply paths to a plurality of beds according to the present disclosure is executed by a computing device including a memory for storing one or more instructions and a processor for executing the one or more instructions stored in the memory, and includes the steps of measuring adsorption values ​​in a plurality of beds, determining a fluid supply sequence to the plurality of beds according to the measured adsorption values ​​in the plurality of beds, and opening and closing the valves to control the fluid supply paths according to the determined fluid supply sequence.

[0009] In addition, a computer program stored on a computer-readable recording medium may be further provided to implement the present disclosure.

[0010] In addition, in order to realize the present disclosure, a computer-readable recording medium for recording a computer program can be further provided. Effect of the Invention

[0011] According to an embodiment of the present disclosure, the adsorption value can be managed for each divided bed instead of managing the entire absorber, thereby improving the adsorption efficiency by matching the desorption cycles of the front and rear beds of the absorber.

[0012] Furthermore, according to an embodiment of the present disclosure, the adsorption and desorption cycles of a plurality of beds are unified, thereby making it possible to reduce the operating costs of the entire absorption tower and extend the lifespan of the plurality of beds.

[0013] The effects of the present disclosure are not limited to those mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the following description. [Brief description of the drawings]

[0014] [Figure 1] FIG. 1 is a schematic diagram illustrating a configuration of an absorber tower according to one embodiment of the present disclosure. [Diagram 2] FIG. 2 is a block diagram illustrating a schematic configuration of an absorption tower that controls fluid supply paths to a plurality of beds according to an embodiment of the present disclosure. [Diagram 3] FIG. 3 is a schematic diagram illustrating fluid flow in a first mode of an absorber tower controlling fluid supply paths to multiple beds according to one embodiment of the present disclosure. [Figure 4] FIG. 4 is a schematic diagram illustrating fluid flow in a second mode of an absorber tower controlling fluid supply paths to multiple beds according to one embodiment of the present disclosure. [Diagram 5] FIG. 5 is a schematic diagram illustrating fluid flow in a third mode of an absorber tower controlling fluid supply paths to multiple beds according to one embodiment of the present disclosure. [Figure 6]FIG. 6 is a flowchart of a drive control method for an absorption tower for controlling fluid supply paths to a plurality of beds according to the present disclosure. [Figure 7] FIG. 7 is a block diagram illustrating a computing device according to one embodiment of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0015] 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.

[0016] 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.

[0017] 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.

[0018] 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.

[0019] The terms "first", "second", etc. are used only to distinguish one component from another component, and are not intended to limit the components.

[0020] The singular expression includes the plural expression unless the context clearly indicates otherwise.

[0021] 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.

[0022] Hereinafter, the working principle and embodiments of the present disclosure will be described with reference to the accompanying drawings.

[0023] In this specification, the term "device according to the present disclosure" includes all of various devices capable of performing computation and providing a result to a user. For example, the device according to an embodiment of the present disclosure may include all of a computer, a server device, and a portable terminal, and may take any of these forms.

[0024] 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.

[0025] 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, etc.

[0026] 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).

[0027] The functions related to the artificial intelligence according to the present disclosure operate via a processor and a memory. The processor may be configured with one or more processors. In this case, the one or more processors may be general-purpose processors such as CPU, AP, DSP (Digital Signal Processor), graphics dedicated processors such as GPU, VPU (Vision Processing Unit), or artificial intelligence dedicated processors such as NPU. The one or more processors control the processing of input data according to predefined operating rules or artificial intelligence models stored in the memory. In addition, when 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.

[0028] 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 learning data by a learning algorithm, thereby creating predefined behavioral rules or artificial intelligence models that are set to perform a desired characteristic (or purpose). Such learning may be performed in the device itself on which the artificial intelligence according to the present disclosure is executed, or may be performed via a separate server and / or system. Examples of the learning algorithm include supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but are not limited to the above examples.

[0029] The artificial intelligence model may be composed of a plurality of neural network layers. Each of the plurality of neural network layers has a plurality of weight values, and performs a neural network operation through an operation result of a previous layer and an operation between the plurality of weights. The plurality of weights of the plurality of neural network layers may be optimized according to a learning result of the artificial intelligence model. For example, the plurality of weights may be updated so that a loss value or a cost value acquired 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 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, but is not limited to the above examples.

[0030] According to an exemplary embodiment of the present disclosure, the 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. The methodology of artificial intelligence can be classified into supervised learning, in which input data and output data are provided together as training data according to a learning method, so that an answer (output data) to a problem (input data) is determined, unsupervised learning, in which only input data is provided without output data, so that an answer (output data) to a problem (input data) is not determined, and reinforcement learning, in which a reward is given in an external environment every time a certain action is taken in a current state, and learning is performed in a direction to maximize the reward. Artificial intelligence methodologies can also be categorized by 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), etc.

[0031] The device and system may include an artificial intelligence model. The artificial intelligence model may be one artificial intelligence model, or may be embodied as a plurality of artificial intelligence models. The artificial intelligence model may be configured as a neural network (or an artificial neural network) and may include a statistical learning algorithm that mimics biological nerves in machine learning and cognitive science. The neural network may refer to a general model in which artificial neurons (nodes) forming a network by synaptic connections change the strength of synaptic connections through learning and have a problem-solving ability. 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, the device may include an input layer, a hidden layer, and an output layer. The neural network constituting the device may infer a result to be predicted from an arbitrary input by changing the weights of neurons through learning.

[0032] The processor may generate a neural network, train or learn the neural network, perform an operation based on received input data and generate an information signal based on the operation result, or retrain the neural network. The neural network model may include various types of models such as, but not limited to, 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 Boltzman Machines (RBMs), Fully Convolutional Networks, Long Short-Term Memory Networks (LSTMs), and Classification Networks. The processor may include one or more processors for performing an operation based on the neural network model. For example, the neural network may include a deep neural network.

[0033] 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), GAN (Generative Adversarial Network), LSM (Liquid As will be understood by those of ordinary skill in the art, the neural network may include any neural network, including, but not limited to, 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).

[0034] According to an exemplary embodiment of the present disclosure, the processor may implement a number of neural networks, including Convolution Neural Networks (CNNs) such as GoogleNet, AlexNet, and VGG Network, 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 Boltzman 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, Text Analysis, Dialog System, GPT-3, GPT-4 for Natural Language Processing, Visual Analytics, Visual Understanding, Video Synthesis, ResNet for Data Intelligence, Anomaly Detection, Prediction, Time-Series 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.

[0035] Fig. 1 is a schematic diagram showing a configuration of an absorber 100 according to an embodiment of the present disclosure. Hereinafter, the absorber 100 that controls fluid supply paths to a plurality of beds and a control method thereof will be specifically described with reference to Figs. 2 to 6.

[0036] 1, an absorption tower 100 according to the present disclosure can include a plurality of beds T1, T2, and T3 arranged therein, an inlet 30 connected to one side, and an outlet 40 connected to the other side. A feed containing ammonia, nitrogen, and hydrogen is supplied through the inlet 30, ammonia is adsorbed through an adsorption process, and a product containing nitrogen and hydrogen is discharged through the outlet 40.

[0037] In other words, the absorption tower 100 receives air (Feed) containing ammonia, volatile organic compounds (VOCs), etc., and applies vapor recovery methods such as recompression, condensation, and activated carbon adsorption as the air passes through the first bed T1 to the last bed T3, thereby discharging air (Product) from which the ammonia, volatile organic compounds (VOCs), etc. have been removed.

[0038] For example, to regenerate a used absorber 100, a desorption gas can be introduced into the absorber 100 to desorb the ammonia in the absorber 100, and then some or all of the desorption gas can be discharged together with the desorbed ammonia. Alternatively, ammonia can be desorbed and removed using vacuum or heat without the desorption gas.

[0039] Therefore, although not shown in the figure, a separate exhaust pipe can be connected to each of the beds T1, T2, and T3 in order to remove to the outside a part or all of the ammonia and the desorbed gas desorbed by the desorption process in each of the beds T1, T2, and T3, or the ammonia by-product.

[0040] The absorption tower 100 can be divided into an area from a position close to the inlet 30 where the feed is input to the outlet 40 where the product is discharged, and can be classified into a first bed T1, a second bed T2, a third bed T3, etc., and a sensor unit 110 can be provided for each classified bed. The number of beds can be changed, and the sensor unit 110 can include at least one of a pressure sensor that measures the pressure in the absorption tower 10, a temperature sensor that measures the temperature in the absorption tower 10, a humidity sensor that measures the humidity in the absorption tower 10, a gas sensor that measures the gas components in the absorption tower 10 to inspect the purity of the extracted hydrogen, and an adsorbent sensor.

[0041] When the ammonia concentration measured by the sensor unit 110 in the first bed T1 is equal to or greater than a preset value, or when the value measured in the first bed T1 and the value measured in the third bed T3 are equal to or greater than a preset value, the computing device 120 (see FIG. 2) that processes the operation of the absorption tower 100 determines that the adsorbent placed in the first bed T1 has become saturated and can perform a desorption process.

[0042] The computing device 120 (see FIG. 2) can use the sensor unit 110 to check whether the pressure, temperature, etc. are being maintained within a certain range to maintain the adsorption efficiency, and can calculate the result of the hydrogen extraction efficiency using the humidity or hydrogen purity.

[0043] Furthermore, the adsorbent sensor of the sensor unit 110 can measure the state of the adsorbent in the absorption tower, evaluate the life and performance of the adsorbent, and obtain information that can be used to predict when to replace the adsorbent.

[0044] Hereinafter, the gas supplied to or discharged from the Feed or Product will be collectively referred to as the fluid.

[0045] The absorption tower 10 may further include a plurality of connecting pipes 31, 32, 33, 41, 42, 43 that connect the inlet 30, the outlet 40, and at least two or more of the plurality of beds T1, T2, T3. The connecting pipes may include supply pipes 31, 32, 33 that supply a fluid, and discharge pipes 41, 42, 43 that discharge the fluid.

[0046] Specifically, a supply pipe 31 for supplying fluid to the first bed T1, a supply pipe 32 for supplying fluid to the second bed T2, and a supply pipe 33 for supplying fluid to the third bed T3 can be connected.

[0047] Also, a branch pipe 60 can be arranged so that the fluid supplied from the suction port 30 is supplied to supply pipes 31, 32, and 33 connected to the beds T1, T2, and T3.

[0048] In one embodiment, as shown in FIG. 1, supply valves 50a, 50b, and 50c may be arranged in the reverse supply pipes 31, 32, and 33 between the beds T1, T2, and T3 connected to the branch pipe 60, and the supply valves 50 may be omitted in the forward supply pipes 31, 32, and 33 that perform the first mode.

[0049] In one embodiment, all connections may have supply valves 50 or exhaust valves.

[0050] In addition, a discharge pipe 41 for discharging fluid from the first bed T1, a discharge pipe 42 for discharging fluid from the second bed T2, and a discharge pipe 43 for discharging fluid from the third bed T3 can be connected.

[0051] On the other hand, the discharge pipe 41 discharged from the first bed T1 may be the supply pipe 32 that supplies fluid to the second bed 52, and the discharge pipe 42 discharged from the second bed T2 may be the supply pipe 33 that supplies fluid to the third bed T3. In Fig. 1, the supply pipes 31, 32, 33 and the discharge pipes 41, 42, 43 are shown separately, but these may be connecting pipes that allow fluid to flow separately in each mode, or the same connecting pipes may be shown separated according to function.

[0052] Although only three beds are shown in FIG. 1, two or more beds may be arranged depending on the adsorption process and circumstances.

[0053] A valve may be connected to each connecting tube, as shown in Figure 1. The valve may include a supply valve 50 or a discharge valve (not shown).

[0054] When adsorption is performed in each bed, the supply valve 50 can be opened to supply a fluid such as a feed. When the adsorption value in each bed becomes high and desorption is performed, the supply valve 50 and the discharge valve can be closed.

[0055] In FIG. 1, supply valves 50 are shown only on some of the supply pipes, but supply valves 50a, 50b, 50c can be arranged on the supply pipes 31, 32, 33 connecting each bed T1, T2, T3, or on some or all of the supply pipes connecting the suction port 30 to each bed T1, T2, T3.

[0056] Although exhaust valves are not shown in FIG. 1, exhaust valves may be placed in the exhaust pipes 41, 42, 43 connecting between each of the beds T1, T2, T3, or in some or all of the exhaust pipes connecting the exhaust port 40 to each of the beds T1, T2, T3.

[0057] As shown in FIG. 2, an absorption tower 100 that controls a fluid supply path to a plurality of beds according to the present disclosure includes a sensor unit 110 that measures conditions within each of the beds T1, T2, and T3, a computing device 120 that includes a memory 16 that stores one or more instructions and a processor 14 that executes the one or more instructions stored in the memory 16, and a drive unit 130 that drives the absorption tower 100 in accordance with adsorption and desorption periods that are set based on conditions within the absorption tower 100.

[0058] The sensor unit 110 can be provided in each of a plurality of beds that are separated and partitioned inside the absorption tower 100 .

[0059] The processor 14 and the memory 16 may be disposed together or respectively in one or more computing devices 120. The computing device 120 may be used to control the operation of the absorber tower 100 so that the efficiency of the absorber tower 100 is maintained at or above a certain level.

[0060] The user interface 140 can receive user input for the parameters required to run the absorber tower 100 .

[0061] Additionally, the user interface 140 can provide an alarm to the user when an abnormal condition occurs and can receive information regarding the abnormal condition handling process.

[0062] The display unit 150 can provide an alarm related to the operation of the absorber 100, and can display the real-time monitoring of the operating state of the absorber 100. The display unit 150 can display values ​​measured by the sensor unit 110, and can display the current progress of the desorption process.

[0063] Additionally, the absorber tower 100 of the present disclosure may further include an artificial intelligence model that may be co-located with the computing device 120 or connected via a separate server or the cloud.

[0064] In one embodiment, the processor 14 uses a trained artificial intelligence model to output corresponding adsorption and desorption periods based on the sensing data of the sensor unit 110, and when the sensing data is within a preset optimal range, it can transmit a command to the drive unit 130 to drive the absorption tower based on the output adsorption and desorption periods.

[0065] Alternatively, when the sensing data exceeds the optimum range, the processor 14 may transmit a command to the driving unit 130 to execute an abnormal state processing process according to a preset exception adsorption period and exception desorption period.

[0066] The absorption tower 100 according to the present disclosure can identify the internal state of the bed at which the adsorption process cannot proceed any further based on the data collected by the sensor unit 110, and can detect in real time the point at which the desorption process needs to be carried out by using an artificial intelligence model that has learned the steady states regarding the pressure, temperature, humidity, concentration, etc. of the gas in the absorption tower 100 at each point in the adsorption process.

[0067] This makes it possible to manage the adsorption and desorption of the adsorbent so that the adsorption and desorption cycles of the entire beds are unified by learning the adsorption and desorption cycles of each bed.

[0068] The processor 14 repeatedly performs self-diagnosis at a preset period to determine whether the sensing data measured by the sensor unit 110 deviates from an optimal range or whether the pattern of the previous sensing data differs from that of the subsequent sensing data, and can detect the occurrence of an abnormal state in real time using an artificial intelligence model.

[0069] Furthermore, when performing self-diagnosis, the processor 14 detects not only abnormal conditions in the absorption tower 100 but also a sensor error in at least one sensor of the sensor unit 110 and a communication error between the sensor unit 110 and the processor 14. When a sensor error or a communication error is detected, a command to stop driving the absorption tower 100 can be transmitted to the drive unit 130.

[0070] In general, from the first bed T1 to the third bed T3, the contact time with the feed becomes slower and the timing of adsorbent replacement becomes later, so that the timing of adsorbent replacement for each bed T1, T2, T3 differs.

[0071] For example, the adsorbent in the first bed T1 may be nearing the time for replacement, whereas the adsorbent in the third bed T3 may still have plenty of time left for replacement.

[0072] The absorber 100 according to the present disclosure can perform the first mode as shown in FIG. 3 when there is sufficient time left until replacement of each of the beds T1, T2, and T3.

[0073] Alternatively, when the adsorption value of the first bed T1 decreases and the adsorption value of the adsorbent reaches saturation, the second mode shown in FIG. 4 or the third mode shown in FIG. 5 can be carried out.

[0074] Hereinafter, the connecting pipes shown in Figs. 3 to 5 are connecting pipes through which fluid flows, and connecting pipes not shown are in a state in which the supply valve 50 is shut off and no fluid flows through them.

[0075] As shown in FIG. 3, when the adsorption value measured by the sensor unit 110 of each bed T1, T2, T3 is less than a preset threshold value, the processor 12 of the absorption tower 100 according to the present disclosure determines that the adsorbent is not yet saturated and the adsorption process is executable, and can perform the first mode.

[0076] In the first mode, adsorption can be performed in a forward direction in which a fluid flows in through the adsorption port 30 and is discharged to the discharge port 40 via the first bed T1, the second bed T2, and the third bed T3.

[0077] The processor 12 can close the supply valves 50b, 50c connecting the suction port 30 or the branch pipe 60 to the second bed T2 or the third bed T3, and open the supply valve 50a connected to the first bed T1.

[0078] In contrast, as shown in FIG. 4, when the adsorption value in the first bed T1 measured by the sensor unit 110 of the first bed T1 exceeds a preset threshold value, the processor 12 of the absorption tower 100 according to the present disclosure determines that the adsorbent in the first bed T1 is saturated and the adsorption process cannot be performed, and can perform the second mode.

[0079] In addition, if the difference between the adsorption value in the first bed T1 measured by the sensor unit 110 of the first bed T1 and the adsorption value in the third bed T3 measured by the sensor unit 110 of the third bed T3 exceeds a preset threshold value, it is determined that the adsorbent in the first bed T1 is saturated and the adsorption process cannot be performed, and the second mode can be performed.

[0080] In the second mode, reverse adsorption can be performed in which the fluid flowing in through the adsorption port 30 passes through the third bed T3, the second bed T2, and the first bed T1 and is discharged to the discharge port 40.

[0081] The processor 12 can control the supply of fluid in the reverse direction from the third bed T3 by closing the supply valve 50a connected between the branch pipe 60 and the first bed T1 and opening the supply valve 50c connected between the branch pipe 60 and the third bed T3.

[0082] In other words, the forward direction is from the bottom to the top of the absorber tower 100, and the reverse direction is from the top to the bottom, which is the reverse order of the sequentially arranged beds.

[0083] In one embodiment, by closing both supply valves 50b arranged on the supply pipe 32, the fluid in the adsorption port 30 can be caused to flow immediately to the third bed T3 without flowing to the first bed T1 and the second bed T2.

[0084] As a result, the adsorption process is performed in the forward direction in the first mode, and when the adsorption value of the adsorbent in the first bed T1 is high or when the difference in adsorption rate with the third bed T3 becomes large, the adsorption process is performed in the reverse direction in the second mode, thereby making it possible to maintain the adsorption efficiency of the entire beds T1, T2, and T3 at a constant level and efficiently manage it.

[0085] In another embodiment, as shown in FIG. 5, when the adsorption value in the first bed T1 measured by the sensor unit 110 of the first bed T1 exceeds a preset threshold value, the processor 12 of the absorption tower 100 of the present disclosure determines that the adsorbent in the first bed T1 is saturated and the adsorption process cannot be performed, and can perform the third mode.

[0086] In addition, if the difference between the adsorption value in the first bed T1 measured by the sensor unit 110 of the first bed T1 and the adsorption value in the third bed T3 measured by the sensor unit 110 of the third bed T3 exceeds a preset threshold value, it is determined that the adsorbent in the first bed T1 is saturated and the adsorption process cannot be performed, and the third mode can be performed.

[0087] In the third mode shown in FIG. 5(a), the supply valve 50a connected between the suction port 30 or the branch pipe 60 and the first bed T1 is closed, and the supply valve 50b connected between the suction port 30 or the branch pipe 60 and the second bed T2, or the supply valve 50c connected between the suction port 30 or the branch pipe 60 and the third bed T3 is opened to supply fluid in the reverse direction.

[0088] Alternatively, in a third mode shown in FIG. 5(b), the supply valve 50a connected between the suction port 30 or the branch pipe 60 and the first bed T1 is closed, and the supply valve 50b connected between the suction port 30 or the branch pipe 60 and the second bed T2, or the supply valve 50c connected between the suction port 30 or the branch pipe 60 and the third bed T3 is opened to supply fluid in the forward direction.

[0089] The control according to Fig. 5(a) and Fig. 5(b) can be selected by additionally considering the difference in the suction value between the second bed T2 and the third bed T3. In other words, the processor 14 can perform the control according to Fig. 5(a) when the difference in the suction value exceeds a preset value, and can perform the control according to Fig. 5(b) when the difference is equal to or less than the preset value.

[0090] This allows the desorption step to be performed in the first bed T1, and the adsorption step to be performed in the second bed T2 or the third bed T3. The supply valve 50a or the discharge valve (not shown) of the first bed T1 is closed to prevent the inflow or outflow of fluid, and the supply valves 50b, 50c or the discharge valves (not shown) of the second bed T2 and the third bed T3 are opened to allow the fluid to flow in the reverse or forward direction.

[0091] Meanwhile, the processor 14 of the absorption tower 100 according to the present disclosure can open the supply valve 50a connected between the branch pipe 60 and the first bed T1 when the adsorption value measured by the sensor unit 110 in the first bed T1 again becomes equal to or lower than the threshold value. The processor 14 can control the plurality of beds T1, T2, and T3 including the first bed T1 to perform the adsorption process sequentially. This is to prevent an abnormal state due to an error in the sensor unit 110 by returning to the first mode of FIG. 3.

[0092] In one embodiment, the beds T1, T2, and T3 may each be provided with a different type of adsorbent. The adsorbent efficiency of the first bed T1 may be lower than that of the second bed T2, and the adsorbent efficiency of the second bed T2 may be lower than that of the third bed T3. The first bed T1, which is in contact with ammonia at a higher concentration, may use an inexpensive adsorbent at the expense of low adsorption efficiency, and the second bed T2 and the third bed T3, which are in contact with ammonia at lower concentrations, may use more expensive adsorbents at the expense of successively higher adsorption efficiency.

[0093] In one embodiment, the type of adsorbent may be determined taking into consideration the temperature conditions at the top or bottom of the absorption tower 100, and the beds may be arranged in ascending order of the desorption temperatures of the adsorbents.

[0094] This allows the entire bed system to be divided into a plurality of beds T1, T2, and T3, and a sensor unit 110 to be arranged for each bed, allowing the adsorption process to be performed with optimal efficiency by managing the adsorbent efficiency in the upper and lower parts of the absorption tower 100 collectively. In this case, optimal efficiency means a state in which the adsorption efficiency can be maintained at a specific level or higher while reducing costs by delaying the replacement of the adsorbent as long as possible.

[0095] Therefore, the absorber tower 100 according to the present disclosure can identify the internal conditions of each internal compartment and operate to perform separate adsorption and desorption processes accordingly.

[0096] In one embodiment, when an artificial intelligence model is used to detect that the temperature, humidity, or pressure is outside the range predicted based on the current amount of adsorbent and the process progress in the absorption tower 100, an alarm can be provided by the display unit 150 indicating that the mode must be changed from the first mode to the second mode or the third mode.

[0097] An absorber tower 100 containing multiple beds can be divided into multiple sections, or multiple absorber towers 100 can be connected in series, ultimately separating the spaces in which each adsorbent is placed.

[0098] In addition, the absorption tower 100 according to the present disclosure can use an artificial intelligence model to determine the feed supply procedure between multiple beds T1, T2, and T3 and the generation of a new feed supply path excluding the bed to be regenerated at the predicted time when the adsorbent regeneration cycle including the desorption process will begin.

[0099] Specifically, when feed is supplied to the inlet 30 of the absorber 100 and exits at the outlet 40, a pressure drop occurs in which the pressure at the inlet 30 is higher than the pressure at the outlet 40. In other words, when the pressure at the inlet 30 is higher than the pressure at the outlet 40, a normal fluid flow can be formed, and for this purpose, it is preferable that the difference between the pressure P1 at the inlet 30 and the pressure P2 at the outlet 40 be maintained constant.

[0100] However, if the flow velocity or flow rate increases due to the internal conditions of the absorption tower 100, the gap between P1 and P2 increases, and the value of P1-P2 continues to increase. Considering an environment in which the magnitude of P1 cannot be increased infinitely, the gap between P1 and 2P must be controlled.

[0101] Therefore, the absorption tower 100 according to the present disclosure can command dynamic structural changes in real time, such as connecting multiple beds additionally or connecting serially connected beds in parallel, when an internal situation occurs in which the flow rate or flow rate increases based on the artificial intelligence model. By controlling the flow rate and flow rate of the fluid by performing dynamic structural changes in real time for the multiple beds T1, T2, and T3, it is possible to prevent the value of P1-P2 from becoming large and to provide a stable fluid supply without increasing the value of P1.

[0102] In one embodiment, the artificial intelligence model can monitor the state of each bed in real time using a sensor unit 110 provided in multiple beds T1, T2, and T3.

[0103] In addition, in one embodiment, the artificial intelligence model can determine whether the flow rate or flow rate in the bed is equal to or greater than a threshold value based on the flow rate or flow rate value of the fluid acquired from the sensor unit 110. In this case, the sensor unit 110 can include a flow rate sensor or a flow rate sensor.

[0104] In one embodiment, the artificial intelligence model can convert the arrangement of multiple beds T1, T2, T3 from a series connection to a parallel connection when the flow rate or flow velocity in the bed is equal to or greater than a threshold flow rate or flow velocity.

[0105] Alternatively, in one embodiment, the artificial intelligence model can determine a fluid supply path including an additional bed by connecting an additional bed when the flow rate or flow velocity in the bed is equal to or greater than a threshold value, or when the pressure difference between the inlet and outlet of the absorber is equal to or greater than a threshold value. The artificial intelligence model can determine a final plan among multiple plans for controlling the flow rate or flow velocity to reduce the flow rate or flow velocity, taking into consideration resources such as time and cost based on the internal situation of the absorber 100. This allows the flow rate or flow velocity in multiple beds T1, T2, and T3 to be controlled.

[0106] On the other hand, the drive control method of the absorption tower 100 according to the present disclosure can be executed by a computing device 120 including a memory 16 that stores one or more instructions and a processor 14 that executes the one or more instructions stored in the memory 16.

[0107] As shown in FIG. 6, the drive control method of the absorption tower 100 according to the present disclosure may include a step (S610) of measuring adsorption values ​​in a plurality of beds T1, T2, and T3, a step (S620) of determining a fluid supply sequence to the plurality of beds T1, T2, and T3 based on the measured adsorption values ​​in the plurality of beds T1, T2, and T3, and a step (S630) of opening and closing the valves according to the determined fluid supply sequence to control the fluid supply path.

[0108] In step S620, if the suction values ​​measured by the sensor units 110 of each bed T1, T2, and T3 are less than a preset threshold value, a forward suction order can be determined in which the material is discharged to the discharge outlet 40 via the first bed T1, the second bed T2, and the third bed T3.

[0109] In step S630, the first mode can be executed to control the supply valve 50 or the discharge valve so that the fluid flowing in from the adsorption port 30 is discharged to the discharge port 40 via the first bed T1, the second bed T2, and the third bed T3.

[0110] Specifically, the supply valves 50b, 50c connecting the suction port 30 or the branch pipe 60 to the second bed T2 or the third bed T3 can be closed, and the supply valve 50a connected to the first bed T1 can be opened.

[0111] In contrast, in step S620, when the suction value in the first bed T1 measured by the sensor unit 110 of the first bed T1 exceeds a preset threshold value, a reverse suction order can be determined in which the material is discharged to the discharge outlet 40 via the third bed T3, the second bed T2, and the first bed T1.

[0112] Alternatively, in step S620, if the difference between the suction value in the first bed T1 measured by the sensor unit 110 of the first bed T1 and the suction value in the third bed T3 measured by the sensor unit 110 of the third bed T3 exceeds a preset threshold, a reverse suction order can be determined in which the liquid is discharged to the discharge outlet 40 via the third bed T3, the second bed T2, and the first bed T1.

[0113] In step S630, the second mode can be executed to control the supply valve 50 or the discharge valve so that the fluid flowing in from the adsorption port 30 is discharged to the discharge port 40 via the third bed T3, the second bed T2, and the first bed T1.

[0114] Specifically, the supply valve 50a connected between the branch pipe 60 and the first bed T1 is closed, and the supply valve 50c connected between the branch pipe 60 and the third bed T3 is opened, so that fluid is supplied in the reverse direction from the third bed T3.

[0115] Alternatively, in step S620, it may be determined that the first bed T1, which has exceeded the adsorption value, is excluded from the adsorption process and the desorption process is performed.

[0116] In step S630, a third mode is performed to close the supply valve 50a connected between the suction port 30 or the branch pipe 60 and the first bed T1, and open the supply valve 50b connected between the suction port 30 or the branch pipe 60 and the second bed T2, or the supply valve 50c connected between the suction port 30 or the branch pipe 60 and the third bed T3 to supply fluid in the reverse or forward direction.

[0117] Matters that overlap with those discussed above will be omitted for the sake of brevity.

[0118] Meanwhile, the adsorption value of each of the beds T1, T2, and T3 can be confirmed using the temperature sensor of the sensor unit 110. The temperature rises as the adsorption becomes more active, and the temperature drops as the adsorption efficiency decreases and the time to replace the adsorbent approaches, so that the change in the state of the adsorbent can be understood from the temperature change. Therefore, if the temperature drops, it can be determined that the adsorption value of the adsorbent has exceeded a preset threshold, and if the temperature remains high, it can be determined that the adsorption value of the adsorbent is equal to or lower than the preset threshold.

[0119] Meanwhile, Fig. 7 illustrates a computing device according to an embodiment of the present disclosure. As shown in Fig. 7, the method of controlling the operation of an absorption tower 100 for controlling fluid supply paths to a plurality of beds according to the present disclosure can be realized in the form of a recording medium storing instructions executable by a computer. The instructions can be stored in the form of program code, and when executed by a processor, a program module can be generated to perform the operations of the disclosed embodiment. The recording medium can be realized as a computer-readable recording medium.

[0120] A computing device 120 of an absorber tower 100 according to the present disclosure may correspond to a computing device 12. Computing device 12 may include at least one processor 14, a computer-readable storage medium 16 containing a program 20, and a communications bus 18. 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 communications interfaces 26.

[0121] The user interface 140 and the display unit 150 of the present disclosure may correspond to one or more I / O interfaces 22 that provide an interface for the I / O device 24. Therefore, each component of the absorber drive control device that controls the flow of fluid by section according to the present disclosure may be all included in the single computing device 12 described above, or each may be embodied in a separate device.

[0122] 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. Thus, the components of the absorber tower 100 that controls the fluid supply paths to the multiple beds of the present disclosure may all be included in the single computing device 12 described above, or may each be embodied in a separate device.

[0123] As described above, the disclosed embodiments have been described with reference to the accompanying drawings. Those skilled in the art will understand that the present disclosure may be implemented in a form different from the disclosed embodiments without changing the technical idea or essential features of the present disclosure. The disclosed embodiments are illustrative and should not be construed as being limited.

Claims

1. 1. An absorber tower including a plurality of beds disposed therein, an inlet connected to one side, and an outlet connected to the other side, A sensor unit disposed in each of the plurality of beds; a plurality of connecting pipes connecting at least two or more of the inlet, the outlet, and the plurality of beds; A plurality of valves connected to the plurality of connecting pipes; a memory storing one or more instructions; a processor for executing the one or more instructions stored in the memory; the processor is configured to determine a fluid supply sequence to the plurality of beds in accordance with the adsorption values ​​in the plurality of beds measured by the sensor unit, and to control the fluid supply paths by opening and closing the valves in accordance with the determined fluid supply sequence, and the absorption tower controls the fluid supply paths to the plurality of beds.

2. the connecting pipe includes branch pipes each connecting the suction port to at least one of the plurality of beds, 2. The absorber tower for controlling a fluid supply path to a plurality of beds according to claim 1, wherein the valve includes a supply valve connected between the branch pipe and at least one of the plurality of beds.

3. The plurality of beds includes at least a first bed arranged in a front row and a second bed arranged in a rear row, The processor, When the adsorption value in the first bed measured by the sensor unit exceeds a preset threshold value, a supply valve connected between the branch pipe and the first bed is closed, and a supply valve connected between the branch pipe and the second bed is opened to supply a fluid in a reverse direction from the second bed, 3. The absorber tower for controlling a fluid supply path to a plurality of beds according to claim 2, wherein the reverse direction is in a reverse order to the sequentially arranged beds.

4. The plurality of beds includes at least a first bed arranged in a front row and a second bed arranged in a rear row, The processor, When a difference in fluid concentration between the first bed and the second bed measured by the sensor unit exceeds a preset threshold value, a supply valve connected between the branch pipe and the first bed is closed, and a supply valve connected between the branch pipe and the second bed is opened to supply fluid in a reverse direction from the second bed; 3. The absorber tower for controlling a fluid supply path to a plurality of beds according to claim 2, wherein the reverse direction is in a reverse order to the sequentially arranged beds.

5. The plurality of beds includes, in a sequentially arranged order, at least a first bed, a second bed, and a third bed; The processor, When the adsorption value in the first bed measured by the sensor unit exceeds a preset threshold value, a supply valve connected to the first bed is closed, and a supply valve connected between the branch pipe and the second bed or the third bed is opened to supply a fluid in a forward direction or a reverse direction; 3. The absorber tower for controlling a fluid supply path to a plurality of beds according to claim 2, wherein the forward direction is an order of sequentially arranged beds, and the reverse direction is an order opposite to the forward direction.

6. 6. The absorption tower controlling fluid supply paths to a plurality of beds according to claim 5, wherein the processor controls the first bed to perform a desorption step and the second bed or the third bed to perform an adsorption step.

7. The processor, 7. The absorption tower controlling fluid supply paths to a plurality of beds according to claim 6, wherein when the adsorption value measured by the sensor unit in the first bed becomes equal to or less than a threshold value, a supply valve connected between the branch pipe and the first bed is opened, and control is performed so that the adsorption process is performed sequentially in the plurality of beds including the first bed.

8. The plurality of beds includes, in a sequentially arranged order, at least a first bed, a second bed, and a third bed; 8. An absorption tower controlling fluid supply paths to a plurality of beds according to claim 7, wherein the plurality of beds are respectively arranged with different types of adsorbents, and the adsorbent efficiency of the first bed is lower than the adsorbent efficiency of the second bed, and the adsorbent efficiency of the second bed is lower than the adsorbent efficiency of the third bed.

9. 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; 9. The absorber tower controlling a fluid supply path to a plurality of beds according to claim 8, wherein the processor transmits a command to a drive unit to stop driving the absorber tower when an error in the at least one sensor is detected or a communication error between the at least one sensor and the processor is detected.

10. The processor is configured to: when a flow rate or velocity in the bed is equal to or greater than a preset threshold value; or when a pressure difference between an inlet and an outlet of an absorber is equal to or greater than a preset threshold value; 2. The absorption tower for controlling fluid supply paths to a plurality of beds according to claim 1, further comprising: an artificial intelligence model for determining a fluid supply sequence or a fluid supply path to control the pressure between the inlet and the outlet; and transmitting a command to change a connection structure of the plurality of beds based on the determined fluid supply sequence or fluid supply path.

11. 1. A method for controlling an absorber tower, the method being 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: The method comprises: measuring the adsorption values ​​in the plurality of beds; determining a fluid supply sequence to the plurality of beds in response to the measured adsorption values ​​in the plurality of beds; and controlling the fluid supply paths by opening and closing valves in accordance with the determined fluid supply sequence.

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