Absorption tower and its drive control method for controlling fluid supply paths to multiple beds

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 a driving control device and a control method for an absorption tower, and more specifically, to an absorption tower that controls a fluid supply path to a plurality of beds according to adsorption values in each section and a driving control method thereof.

Background Art

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

[0003] Conventionally, when an absorption tower is driven to adsorb and remove ammonia, and when the adsorption exceeds a specific level and the removal efficiency of the absorption tower decreases, the ammonia is desorbed and controlled to maintain a state where it can be adsorbed again.

[0004] However, the section located at the forefront of the absorption tower adsorbs the fluid with the highest ammonia concentration, and the section located at the last row adsorbs the fluid with the lowest ammonia concentration. Therefore, there was a large difference in the adsorption efficiency or consumption rate of the adsorbent between the section located at the forefront and the section located at the last row. In particular, the section located at the forefront rapidly consumed the adsorbent, and the adsorption / desorption cycle was shorter than that of other sections, resulting in a short lifespan and replacement cycle.

Summary of the Invention

Problems to be Solved by the Invention

[0005] The problem of the present disclosure is to improve the adsorption efficiency, reduce the operation cost of the entire absorption tower, and extend the lifespan of a plurality of beds by controlling so that the order of supplying fluid is different according to the adsorption values in each section. Accordingly, an absorption tower that controls a fluid supply path to a plurality of beds and a driving control method thereof are provided, which can contribute to environmental friendliness and enable the production of clean energy.

[0006] The problems that this disclosure seeks to solve are not limited to those mentioned above, and any other problems not mentioned can be clearly understood by an ordinary engineer from the following description. [Means for solving the problem]

[0007] The absorption tower for controlling fluid supply paths to multiple beds according to this disclosure includes a plurality of beds arranged inside, an intake port 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 intake port, the outlet and at least two of the plurality of beds, a plurality of valves connected to the plurality of connecting pipes, a memory for storing one or more instructions, and a processor for executing the one or more instructions stored in the memory, wherein the processor can be configured to determine the order in which fluid is supplied to the plurality of beds according to the adsorption values ​​in the plurality of beds measured by the sensor unit, and to control the fluid supply path by opening and closing the valves according to the determined fluid supply order.

[0008] The drive control method for an absorption tower that controls fluid supply paths to multiple beds according to this disclosure is performed 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 the multiple beds; determining the order in which to supply fluid to the multiple beds according to the measured adsorption values ​​in the multiple beds; and controlling the fluid supply paths by opening and closing the valves according to the determined fluid supply order.

[0009] In addition, to realize this disclosure, we can further provide computer programs stored on computer-readable recording media.

[0010] In addition, to realize this disclosure, a computer-readable recording medium for recording computer programs can be further provided. [Effects of the Invention]

[0011] According to one embodiment of the present disclosure, instead of managing the absorption tower as a whole, the adsorption values ​​can be managed for each divided bed. This makes it possible to improve adsorption efficiency by matching the desorption cycles of the front row and back row beds of the absorption tower.

[0012] Furthermore, according to one embodiment of this disclosure, by unifying the adsorption / desorption cycles of multiple beds, the operating costs of the entire absorption tower can be reduced and the lifespan of the multiple beds can be extended.

[0013] The effects of this disclosure are not limited to those mentioned above, and any other effects not mentioned can be clearly understood by an ordinary person of the art from the following description. [Brief explanation of the drawing]

[0014] [Figure 1] Figure 1 is a schematic diagram illustrating the configuration of an absorption tower according to one embodiment of the present disclosure. [Figure 2] Figure 2 is a schematic block diagram showing the configuration of an absorption tower that controls fluid supply paths to multiple beds according to one embodiment of the present disclosure. [Figure 3] Figure 3 is a schematic diagram showing the fluid flow in a first mode of an absorption tower that controls fluid supply paths to multiple beds according to one embodiment of the present disclosure. [Figure 4] Figure 4 is a schematic diagram showing the fluid flow in a second mode of an absorption tower that controls the fluid supply paths to multiple beds according to one embodiment of the present disclosure. [Figure 5] Figure 5 is a schematic diagram showing the fluid flow in a third mode of an absorption tower that controls the fluid supply paths to multiple beds according to one embodiment of the present disclosure. [Figure 6]Figure 6 is a flowchart of a drive control method for an absorption tower that controls fluid supply paths to multiple beds according to the present disclosure. [Figure 7] Figure 7 is a block diagram showing a computing device according to one embodiment of the present disclosure. [Modes for carrying out the invention]

[0015] Throughout this disclosure, the same reference numerals refer to the same component. This disclosure does not describe all elements of the embodiments, and general content in the art to which this disclosure belongs or content that is redundant in the embodiments is omitted. The terms “parts, modules, components, blocks” as used in this specification may be embodied in software or hardware, and in embodiments, multiple “parts, modules, components, blocks” may be embodied as a single component, or a single “part, module, component, block” may include multiple components.

[0016] In the specification as a whole, "connection" of one part to another includes not only direct connection but also indirect connection. Indirect connection includes connection via a wireless communication network.

[0017] Furthermore, when a part "includes" a certain component, unless otherwise stated, it does not exclude other components, but rather means that it may include other components.

[0018] Throughout the specification, the statement that one component is located "on top of" another component includes not only cases where one component is in contact with another component, but also cases where yet another component exists between the two components.

[0019] Terms such as "first," "second," etc., are used to distinguish one component from another, and do not limit the components.

[0020] A singular expression includes plural forms unless there is a clear exception in the context.

[0021] The identification codes in each step are used for ease of explanation and do not explain the order of each step. Unless a specific order is clearly stated in the context, each step can be implemented in a different way from the stated order.

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

[0023] The "device according to the present disclosure" in this specification includes all various devices that can perform arithmetic processing and provide results to the 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 be in any form.

[0024] Here, the computer may include, for example, a notebook computer equipped with a web browser, a desktop, a laptop, a tablet PC, a slate PC, and the like.

[0025] The server device is a server that communicates with an external device 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, and a web server, and the like.

[0026] The aforementioned 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 (Registered Trademark) (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 artificial intelligence and related functions described herein operate via a processor and memory. The processor can consist of one or more processors. In this case, one or more processors may be general-purpose processors such as CPUs, APs, and DSPs (Digital Signal Processors), graphics-dedicated processors such as GPUs and VPUs (Vision Processing Units), or artificial intelligence-dedicated processors such as NPUs. One or more processors are controlled to process input data according to predefined operating rules or artificial intelligence models stored in memory. Furthermore, if one or more processors are artificial intelligence-dedicated processors, these processors can be designed with a hardware structure specialized for processing a particular 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 the basic artificial intelligence model is trained using a learning algorithm with a large amount of training data to create predefined behavioral rules or artificial intelligence models configured to perform desired characteristics (or objectives). Such learning may be performed on the device on which the artificial intelligence relating to this disclosure is executed, or it may be performed via a separate server and / or system. Examples of learning algorithms include, but are not limited to, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning.

[0029] An artificial intelligence model can consist of multiple neural network layers. Each of these neural network layers has multiple weight values, and neural network operations are performed through calculations between the results of calculations in previous layers and the multiple weights. The multiple weights of the multiple neural network layers can be optimized based on the learning results of the artificial intelligence model. For example, the multiple weights can be updated so that the loss value or cost value acquired from the artificial intelligence model during the learning process decreases or is minimized. Artificial neural networks can include deep neural networks (DNNs), such as CNNs (Convolutional Neural Networks), DNNs (Deep Neural Networks), RNNs (Recurrent Neural Networks), RBMs (Restricted Boltzmann Machines), DBNs (Deep Belief Networks), BRDNNs (Bidirectional Recurrent Deep Neural Networks), or Deep Q-Networks, but are not limited to the examples mentioned above.

[0030] According to exemplary embodiments of this disclosure, a processor can embody artificial intelligence. Artificial intelligence refers to machine learning methods based on artificial neural networks that mimic human nerve cells (biological neurons) to enable a machine to learn. Methodologies for artificial intelligence can be classified into supervised learning, in which input data and output data are provided together as training data according to the learning method, thereby determining the answer (output data) to the problem (input data); unsupervised learning, in which only input data is provided without output data, and the answer (output data) to the problem (input data) is not determined; and reinforcement learning, in which a reward is given in the external environment each time an action is taken in the current state, and learning is advanced in a direction that maximizes such rewards. Furthermore, artificial intelligence methodologies can be categorized by their architecture, which is the structure of the learning model. Widely used deep learning technologies can be classified into architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformers, and Generative Adversarial Networks (GANs).

[0031] This device and system may include an artificial intelligence (AI) model. The AI ​​model may be a single AI model or a combination of multiple AI models. The AI ​​model may consist of a neural network (or artificial neural network) and may include statistical learning algorithms that mimic biological nerves in machine learning and cognitive science. A neural network can refer to any model in which artificial neurons (nodes) formed by synaptic connections change the strength of their synaptic connections through learning, thereby possessing problem-solving capabilities. Neurons in a neural network may include combinations of weights or biases. A neural network may include one or more layers, each consisting 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 can infer a desired output from an arbitrary input by changing the weights of its neurons through learning.

[0032] A processor can generate neural networks, train or learn neural networks, perform calculations based on received input data and generate information signals based on the results, or retrain neural networks. Neural network models can include, but are not limited to, a wide variety of models such as CNNs (Convolutional Neural Networks) like GoogleNet, AlexNet, and VGG Network, R-CNNs (Region with Convolutional Neural Networks), RPNs (Region Proposal Networks), RNNs (Recurrent Neural Networks), S-DNNs (Stacking-based Deep Neural Networks), S-SDNNs (State-Space Dynamic Neural Networks), Deconvolution Networks, DBNs (Deep Belief Networks), RBMs (Restricted Boltzman Machines), Fully Convolutional Networks, LSTMs (Long Short-Term Memory) Networks, and Classification Networks. A processor can include one or more processors for performing calculations by neural network models. For example, neural networks can include deep neural networks.

[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), and LSM (Liquid It is reasonable for any engineer to understand that this can include, but is not limited to, any neural network, including, State Machines, ELMs (Extreme Learning Machines), ESNs (Echo State Networks), DRNs (Deep Residual Networks), DNCs (Differentiable Neural Computers), NTMs (Neural Turning Machines), Capsule Networks (CNs), Kohonen Networks (KNs), and Attention Networks (ANs).

[0034] According to exemplary embodiments of this disclosure, the processor supports various technologies such as GoogleNet, AlexNet, VGG Network, CNN (Convolutional Neural Network), R-CNN (Region with Convolutional Neural Network), RPN (Region Proposal Network), RNN (Recurrent Neural Network), S-DNN (Stacking-based Deep Neural Network), S-SDNN (State-Space Dynamic Neural Network), Deconvolution Network, DBN (Deep Belief Network), RBM (Restricted Boltzman Machine), Fully Convolutional Network, LSTM (Long Short-Term Memory) Network, Classification Network, Generative Modeling, eExplainable 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 for vision processing, ResNet data intelligence, Anomaly Detection, Prediction, and Time-Series. A variety of artificial intelligence structures and algorithms, such as Forecasting, Optimization, Recommendation, and Data Creation, can be used, but are not limited to these. Embodiments of this disclosure will be described in detail below with reference to the attached drawings.

[0035] Figure 1 schematically shows the configuration of an absorption tower 100 according to one embodiment of the present disclosure. Hereinafter, the absorption tower 100 that controls the fluid supply paths to multiple beds and the control method thereof will be specifically described with reference to Figures 2 to 6.

[0036] As shown in Figure 1, the absorption tower 100 according to this disclosure may include a plurality of beds T1, T2, T3 arranged inside, an intake port 30 connected to one side, and an outlet port 40 connected to the other side. Feed containing ammonia, nitrogen, and hydrogen can be supplied through the intake port 30, ammonia can be adsorbed in the adsorption process, and a product containing nitrogen and hydrogen can be discharged through the outlet port 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 from the first bed T1 to the last bed T3, thereby discharging air (Product) from which ammonia, volatile organic compounds (VOCs), etc. have been removed.

[0038] For example, to regenerate a spent absorption tower 100, a desorption gas can be introduced into the absorption tower 100 to desorb the ammonia inside, and then some or all of the desorption gas can be discharged along with the desorbed ammonia. Alternatively, ammonia can be desorbed and removed using vacuum or heat without a desorption gas.

[0039] Therefore, although not shown in the diagram, separate discharge pipes can be connected to each bed T1, T2, and T3 to remove some or all of the ammonia and desorbed gases, or ammonia by-products, that are desorbed by the desorption process in each bed T1, T2, and T3.

[0040] The absorption tower 100 can be divided into regions from a position near the intake port 30 where the feed is input to a position near the outlet port 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 may include at least one of a pressure sensor for measuring the pressure inside the absorption tower 10, a temperature sensor for measuring the temperature inside the absorption tower 10, a humidity sensor for measuring the humidity inside the absorption tower 10, a gas sensor for measuring the gas components inside the absorption tower 10 and inspecting the purity of the extracted hydrogen, and an adsorbent sensor.

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

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

[0043] Furthermore, the adsorbent sensor in the sensor unit 110 measures the state of the adsorbent in the absorption tower, evaluates the lifespan and performance of the adsorbent, and can obtain information that allows for the prediction of when to replace the adsorbent.

[0044] Hereafter, gases supplied to or discharged from the Feed or Product will be collectively referred to as fluids.

[0045] Furthermore, the absorption tower 10 may include a plurality of connecting pipes 31, 32, 33, 41, 42, 43 that connect the intake port 30, the outlet port 40, and at least two of the plurality of beds T1, T2, T3. The connecting pipes may include supply pipes 31, 32, 33 that supply fluid and discharge pipes 41, 42, 43 that discharge 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] Furthermore, branch pipes 60 can be arranged to branch the fluid supplied from the intake port 30 so that it is supplied to the supply pipes 31, 32, and 33 connected to each bed T1, T2, and T3.

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

[0049] In one embodiment, supply valves 50 or discharge valves may be provided in all connecting pipes.

[0050] Furthermore, 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 that discharges from the first bed T1 may also be a supply pipe 32 that supplies fluid to the second bed 52, and the discharge pipe 42 that discharges from the second bed T2 may also be a supply pipe 33 that supplies fluid to the third bed T3. In Figure 1, the supply pipes 31, 32, 33 and the discharge pipes 41, 42, 43 are shown separately, but these may also be connecting pipes that allow fluid to flow separately in each mode, or the same connecting pipe may be shown separated by function.

[0052] Furthermore, although Figure 1 shows only three beds, two or more beds may be arranged depending on the adsorption process and circumstances.

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

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

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

[0056] Although the discharge valve is not shown in Figure 1, a discharge valve can be placed in some or all of the discharge pipes 41, 42, 43 connecting each bed T1, T2, T3, or in some or all of the discharge pipes connecting the discharge port 40 to each bed T1, T2, T3.

[0057] As shown in Figure 2, the absorption tower 100 controlling fluid supply paths to multiple beds according to this disclosure may include a sensor unit 110 for measuring the state within each bed T1, T2, T3; a computing device 120 including a memory 16 for storing one or more instructions and a processor 14 for executing the one or more instructions stored in the memory 16; and a drive unit 130 for driving the absorption tower 100 according to adsorption and desorption cycles set based on the state within the absorption tower 100.

[0058] The sensor unit 110 can be installed in each of the multiple beds separated and partitioned within the absorption tower 100.

[0059] The processor 14 and the memory 16 can be located together or separately in one or more computing devices 120. The computing devices 120 can be used to control the operation of the absorption tower 100 so that its efficiency is maintained at or above a certain level.

[0060] The user interface 140 can receive user input for parameters necessary to drive the absorption tower 100.

[0061] Furthermore, the user interface 140 can provide an alarm to the user when an abnormal condition occurs and can receive input regarding the abnormal condition processing process.

[0062] The display unit 150 can display information such as alarms related to the operation of the absorption tower 100 and real-time monitoring of the operating status of the absorption tower 100. It can also display numerical values ​​measured by the sensor unit 110 and the progress of the current desorption process.

[0063] Furthermore, the absorption tower 100 relating to this disclosure may be located together with the computing device 120 and may further include an artificial intelligence model connected via another server or cloud.

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

[0065] Alternatively, if the sensing data exceeds the optimal range, the processor 14 can transmit an instruction to the drive unit 130 to execute an abnormal state processing process based on a preset exception suction period and exception detachment period.

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

[0067] This allows for the management of adsorption and desorption of adsorbents by learning the adsorption and desorption cycle of each bed and unifying the adsorption and desorption cycle for the entire bed.

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

[0069] Furthermore, when the processor 14 performs self-diagnosis, it detects not only abnormal conditions within the absorption tower 100, but also sensor errors in at least one sensor of the sensor unit 110, and communication errors between the sensor unit 110 and the processor 14. If a sensor error or communication error is detected, it can transmit a command to the drive unit 130 to stop the operation of the absorption tower 100.

[0070] Generally, the contact time with the feed decreases as you move from the first bed T1 to the third bed T3, and the timing of adsorbent replacement decreases accordingly, resulting in different adsorbent replacement timings for each bed T1, T2, and T3.

[0071] For example, the adsorbent in the first bed T1 may be nearing the end of its replacement period, while the adsorbent in the third bed T3 may still have plenty of time before it needs replacing.

[0072] The absorption tower 100 according to this disclosure can perform the first mode as shown in Figure 3 if there is sufficient time remaining before each bed T1, T2, and T3 needs to be replaced.

[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 Figure 4 or the third mode shown in Figure 5 can be performed.

[0074] The connecting pipes shown in Figures 3 to 5 are those through which fluid flows, while the connecting pipes not shown are in a state where the supply valve 50 is shut off and no fluid flows.

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

[0076] In the first mode, forward adsorption is possible, in which the fluid flowing in from the adsorption port 30 is discharged to the outlet 40 after passing through the first bed T1, the second bed T2, and the third bed T3.

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

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

[0079] Furthermore, 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, 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, the fluid flowing in from the adsorption port 30 is discharged to the outlet 40 via the third bed T3, the second bed T2, and the first bed T1, enabling reverse adsorption.

[0081] The processor 12 can control the supply of fluid from the third bed T3 in the reverse direction 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 absorption 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 the supply valve 50b located in the supply pipe 32, the fluid in the adsorption port 30 can be made to flow directly to the third bed T3 without flowing to the first bed T1 and the second bed T2.

[0084] This allows for efficient management of the overall adsorption efficiency of beds T1, T2, and T3 by performing the adsorption process in the forward direction in the first mode, and then performing the adsorption process in the reverse direction in the second mode if the adsorption value of the adsorbent in the first bed T1 is high or if the difference in adsorption rate with the third bed T3 widens significantly.

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

[0086] Furthermore, 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, 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 Figure 5(a), the supply valve 50a connected between the suction port 30 or branch pipe 60 and the first bed T1 is closed, and the supply valve 50b connected between the suction port 30 or branch pipe 60 and the second bed T2, or the supply valve 50c connected between the suction port 30 or branch pipe 60 and the third bed T3 is opened to supply fluid in the reverse direction.

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

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

[0090] This allows the detachment process to be performed in the first bed T1 and the adsorption process to be performed in the second bed T2 or the third bed T3. The supply valve 50a or discharge valve (not shown) of the first bed T1 can be closed to prevent the fluid from flowing in or out, and the supply valves 50b, 50c or discharge valves (not shown) of the second bed T2 and the third bed T3 can be opened to allow the fluid to flow in the reverse or forward direction.

[0091] On the other hand, the processor 14 of the absorption tower 100 according to this 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 falls below a threshold again. The adsorption process can be controlled to be performed sequentially in a plurality of beds T1, T2, T3 including the first bed T1. This is to prevent abnormal conditions due to errors in the sensor unit 110 by returning to the first mode in Figure 3.

[0092] In one embodiment, multiple beds T1, T2, and T3 can each contain different types of adsorbents. The adsorption efficiency of the first bed T1 may be lower than that of the second bed T2, and the adsorption efficiency of the second bed T2 may be lower than that of the third bed T3. The first bed T1, which comes into contact with a higher concentration of ammonia, can use an inexpensive adsorbent with lower adsorption efficiency, while the second beds T2 and the third beds T2, which come into contact with lower concentrations of ammonia, can use increasingly expensive adsorbents with progressively higher adsorption efficiency.

[0093] In one embodiment, the type of adsorbent may be determined by considering the temperature conditions at the top or bottom of the absorption tower 100, and the beds may be arranged in order of increasing desorption temperature of the adsorbent.

[0094] This allows the entire bed to be divided into multiple beds T1, T2, and T3, with a sensor unit 110 placed in each bed to centrally manage the adsorbent efficiency in the upper and lower parts of the absorption tower 100, enabling the adsorption process to be performed at optimal efficiency. In this case, optimal efficiency means a state in which the adsorption efficiency can be maintained at a certain level or higher while reducing costs by delaying the adsorbent replacement time as much as possible.

[0095] Therefore, the absorption tower 100 according to this disclosure can identify the internal state of each internal compartment and thereby be driven to perform separate adsorption and desorption processes.

[0096] In one embodiment, if an artificial intelligence model detects that the current amount of adsorbent and the process progress within the absorption tower 100 are outside the expected temperature, humidity, or pressure range, the display unit 150 can provide an alarm indicating that the system must be switched from the first mode to the second or third mode.

[0097] An absorption tower 100 containing multiple beds can be configured by dividing one absorption tower 100 into multiple sections, or by connecting multiple absorption towers 100 in series to ultimately separate the spaces in which each adsorbent is located.

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

[0099] Specifically, when feed is supplied to the inlet 30 of the absorption tower 100 and exits to the outlet 40, a pressure drop phenomenon occurs where 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 to happen, it is preferable that the difference between the pressure P1 at the inlet 30 and the pressure P2 at the outlet 40 is kept constant.

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

[0101] Therefore, the absorption tower 100 according to this disclosure can, based on an artificial intelligence model, command in real time to perform dynamic structural changes such as connecting additional beds or connecting series-connected beds in parallel when internal conditions occur that increase the flow velocity or flow rate. By controlling the fluid flow velocity and flow rate by performing dynamic structural changes of multiple beds T1, T2, and T3 in real time, it is possible to prevent the value of P1-P2 from becoming large and to enable a stable fluid supply without increasing the value of P1.

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

[0103] In one embodiment, the artificial intelligence model can determine whether the flow rate or velocity in the bed is above a threshold value based on the fluid flow rate or velocity value obtained from the sensor unit 110. In this case, the sensor unit 110 may include a flow rate sensor or a velocity sensor.

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

[0105] Alternatively, in one embodiment, the artificial intelligence model can determine a fluid supply path that includes additional beds, by connecting additional beds if the flow rate or velocity in a bed is above a threshold, or if the pressure difference between the inlet and outlet of the absorption tower is above a threshold, thereby excluding beds that require regeneration. The artificial intelligence model can determine the final solution from among several solutions that control the flow rate or velocity to reduce it, taking into account resources such as time and cost based on the internal conditions of the absorption tower 100. This allows for control of the flow rate or velocity in multiple beds T1, T2, T3.

[0106] On the other hand, the drive control method for the absorption tower 100 according to this disclosure can be executed by a computing device 120 which includes a memory 16 for storing one or more instructions and a processor 14 for executing the one or more instructions stored in the memory 16.

[0107] As shown in Figure 6, the drive control method for the absorption tower 100 according to this disclosure may include the steps of: measuring the adsorption values ​​in a plurality of beds T1, T2, T3 (S610); determining the fluid supply order to the plurality of beds T1, T2, T3 based on the measured adsorption values ​​in the plurality of beds T1, T2, T3 (S620); and controlling the fluid supply path by opening and closing the valves according to the determined fluid supply order (S630).

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

[0109] In step S630, the supply valve 50 or the discharge valve can be controlled so that the fluid flowing in from the adsorption port 30 in the first mode 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 and 50c connecting the intake port 30 or branch pipe 60 to the second bed T2 or 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, if the adsorption value in the first bed T1 measured by the sensor unit 110 of the first bed T1 exceeds a preset threshold, a reverse adsorption sequence can be determined in which the adsorbed material is discharged to the discharge port 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 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, the reverse adsorption order, in which the material is discharged to the discharge port 40 via the third bed T3, second bed T2, and first bed T1, can be determined.

[0113] In step S630, the supply valve 50 or the discharge valve can be controlled so that the fluid flowing in from the adsorption port 30 in the second mode 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 can be closed, and the supply valve 50c connected between the branch pipe 60 and the third bed T3 can be opened to control the supply of fluid from the third bed T3 in the reverse direction.

[0115] Alternatively, in step S620, it may be decided to remove the first bed T1 that exceeds the adsorption value from the adsorption process and perform the desorption process.

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

[0117] For the sake of simplicity, any points that overlap with the above will be omitted.

[0118] On the other hand, the adsorption values ​​for each bed T1, T2, and T3 can be checked using the temperature sensor in the sensor unit 110. As adsorption becomes more active, the temperature rises, and as the adsorption efficiency decreases and the time for adsorbent replacement approaches, the temperature changes indicate changes in the state of the adsorbent. Therefore, when the temperature drops, it can be determined that the adsorption value of the adsorbent has exceeded a preset threshold, and when the temperature remains high, it can be determined that the adsorption value of the adsorbent is below the preset threshold.

[0119] On the other hand, Figure 7 shows a computing device according to one embodiment of the present disclosure. As shown in Figure 7, the drive control method for an absorption tower 100 that controls fluid supply paths to multiple beds according to the present disclosure can be implemented in the form of a recording medium that stores computer-executable instruction words. The instruction words can be stored in the form of program code, and when executed by a processor, a program module can be generated to perform the operation of the embodiment of the disclosure. The recording medium can be implemented as a computer-readable recording medium.

[0120] The computing device 120 of the absorption tower 100 according to this disclosure can 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 providing interfaces for input / output devices 24, and one or more network communication interfaces 26.

[0121] The user interface 140 and display unit 150 of this disclosure can correspond to one or more input / output interfaces 22 that provide an interface for the input / output device 24. Accordingly, each component of the absorption tower drive control device that controls the fluid flow section by section according to this disclosure may be included in the aforementioned single computing device 12, or each may be embodied in separate devices.

[0122] The user interface 140 and display unit 150 of this disclosure can correspond to one or more input / output interfaces 22 that provide an interface for the input / output device 24. Accordingly, each component of the absorption tower 100 that controls the fluid supply paths to the multiple beds according to this disclosure may be included in the aforementioned single computing device 12, or each may be embodied in separate devices.

[0123] As described above, embodiments disclosed have been described with reference to the attached drawings. A person with ordinary skill in the art to which this disclosure belongs will understand that this disclosure may be carried out in a manner different from the disclosed embodiments without altering the technical idea or essential features of this disclosure. The disclosed embodiments are illustrative and should not be construed as restrictive.

Claims

1. An absorption tower comprising multiple beds arranged inside, an intake port connected to one side, and an exhaust port connected to the other side, Sensor units are each placed within the aforementioned plurality of beds, Multiple connecting pipes connecting at least two of the intake port, the discharge port, and the plurality of beds, Multiple valves connected to the aforementioned multiple connecting pipes, A memory that stores one or more instructions, A processor that executes one or more instructions stored in the memory, The processor is configured to determine the order in which gas fluid is supplied to the multiple beds by selecting one of several modes, including a first mode in which gas fluid is supplied in the forward direction of the sequentially arranged multiple beds, a second mode in which gas fluid is supplied in the reverse direction of the sequentially arranged multiple beds, and a third mode in which gas fluid is supplied in either the forward or reverse direction by closing one of the multiple beds, according to the adsorption values ​​in the multiple beds measured by the sensor unit, and to control the gas fluid supply path by opening and closing the valve in accordance with the determined mode. The sensor unit includes a temperature sensor, and the absorption tower controls the gaseous fluid supply path to multiple beds, indirectly obtaining the adsorption values ​​of each of the multiple beds using the temperature sensor.

2. The connecting pipe includes branch pipes that connect the intake port to at least one of the plurality of beds, An absorption tower according to claim 1, wherein the valve controls a gaseous fluid supply path to a plurality of beds, the valve comprising a supply valve connected between the branch pipe and at least one of the plurality of beds.

3. The aforementioned plurality of beds include at least a first bed located in the front row and a second bed located in the back row, The aforementioned processor, If the adsorption value in the first bed measured by the sensor unit exceeds a preset threshold, the supply valve connected between the branch pipe and the first bed is closed, and the supply valve connected between the branch pipe and the second bed is opened, thereby controlling the system to supply gaseous fluid in the reverse direction from the second bed in the second mode. The reverse direction is the reverse order of the sequentially arranged beds, wherein the absorption tower controls the gaseous fluid supply path to a plurality of beds according to claim 2.

4. The aforementioned plurality of beds include at least a first bed located in the front row and a second bed located in the back row, The aforementioned processor, If the difference in gaseous fluid concentration between the first bed and the second bed, as measured by the sensor unit, exceeds a preset threshold, the system controls the system in the second mode by closing the supply valve connected between the branch pipe and the first bed, opening the supply valve connected between the branch pipe and the second bed, and supplying gaseous fluid in the reverse direction from the second bed. The reverse direction is the reverse order of the sequentially arranged beds, wherein the absorption tower controls the gaseous fluid supply path to a plurality of beds according to claim 2.

5. The aforementioned plurality of beds, in the order in which they are arranged sequentially, include at least a first bed, a second bed, and a third bed. The aforementioned processor, In the third mode, if the adsorption value in the first bed measured by the sensor unit exceeds a preset threshold, the supply valve connected to the first bed is closed, and the supply valve connected between the branch pipe and the second bed or the third bed is opened to supply gaseous fluid in the forward or reverse direction. An absorption tower for controlling a gaseous fluid supply path to a plurality of beds according to claim 2, wherein the forward direction is the order of sequentially arranged beds, and the reverse direction is the order reversed from the forward direction.

6. An absorption tower that controls a gaseous fluid supply path to a plurality of beds according to claim 5, wherein the processor controls the first bed to perform a desorption step of desorbing a substance adsorbed from the adsorbent of the first bed, and the second bed or the third bed to perform an adsorption step.

7. The aforementioned processor, An absorption tower for controlling a gaseous fluid supply path to a plurality of beds according to claim 6, wherein when the adsorption value measured by the sensor unit in the first bed falls below a threshold, a supply valve connected between the branch pipe and the first bed is opened, and the adsorption process is controlled to be performed sequentially in a plurality of beds including the first bed.

8. The aforementioned plurality of beds, in the order in which they are arranged sequentially, include at least a first bed, a second bed, and a third bed. An absorption tower for controlling a gaseous fluid supply path to a plurality of beds according to claim 7, wherein each of the plurality of beds is arranged with a different type of adsorbent, the adsorbent efficiency of the first bed is lower than that of the second bed, and the adsorbent efficiency of the second bed is lower than that of the third bed.

9. The sensor unit includes at least one sensor selected from a pressure sensor, a temperature sensor, a humidity sensor, a gas sensor, and an adsorbent sensor. An absorption tower that controls a gaseous fluid supply path to a plurality of beds, according to claim 8, wherein the processor transmits a command to the drive unit to stop driving the absorption tower when an error is detected in the at least one sensor or when a communication error is detected between the at least one sensor and the processor.

10. A method for controlling the drive of an absorption tower, which is performed 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, The aforementioned method, The steps include measuring the adsorption values ​​in multiple beds, The steps include determining the order in which gaseous fluid is supplied to multiple beds according to the adsorption values ​​measured in the multiple beds, The process includes the step of controlling the gas fluid supply path by opening and closing valves according to a determined gas fluid supply sequence, The processor is configured to determine the order in which gas fluid is supplied to the multiple beds by selecting one of several modes, including a first mode in which gas fluid is supplied in the forward direction of the sequentially arranged beds, a second mode in which gas fluid is supplied in the reverse direction of the sequentially arranged beds, and a third mode in which gas fluid is supplied in either the forward or reverse direction by closing one of the multiple beds, according to the adsorption values ​​in the multiple beds measured by the sensor unit, and to control the gas fluid supply path by opening and closing the valve in accordance with the determined mode. A drive control method for an absorption tower that controls a gaseous fluid supply path to multiple beds, wherein the sensor unit includes a temperature sensor, and the adsorption values ​​of each of the multiple beds are indirectly obtained by the temperature sensor.

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