Adsorption tower for controlling fluid supply paths of multiple beds and driving control method thereof

Abstract

The adsorption tower for controlling a fluid supply path of a plurality of beds according to the present disclosure includes a plurality of beds disposed inside, a suction port connected to one side, and a discharge port connected to the other side, the adsorption tower including: a sensor unit disposed inside each of the plurality of beds; a plurality of connection pipes connecting at least two of the suction port, the discharge port, and the plurality of beds; a plurality of valves connected to the plurality of connection pipes; a memory storing one or more instructions; and a processor executing the one or more instructions stored in the memory, the processor configured to determine a fluid supply sequence of the plurality of beds according to adsorption values in the plurality of beds measured by the sensor unit, and open and close the valve to control a fluid supply path according to the determined fluid supply sequence.

Description

Technical Field

[0001] The present application discloses a driving control device and a control method of an adsorption tower, and more particularly, relates to an adsorption tower and a driving control method thereof, which control fluid supply routes of multiple beds according to adsorption values ​​of each section. Background Art

[0002] Ammonia is one of the substances that cause bad odors. It is not only produced in livestock farms, but also in composting facilities, factories and sewage treatment plants that use fertilizers or chemical fibers.

[0003] Conventionally, an adsorption tower is driven to adsorb and remove ammonia, and when the removal efficiency of the adsorption tower decreases due to adsorption of ammonia above a certain level, control is performed to desorb the ammonia to maintain a state in which adsorption can be performed again.

[0004] However, the first row of the adsorption tower adsorbs the fluid with the highest ammonia concentration, and the last row of the adsorption tower adsorbs the fluid with the lowest ammonia concentration, so the adsorption efficiency or depletion rate of the adsorbent between the first row of the adsorption tower and the last row of the adsorption tower differs greatly. In particular, the adsorbent in the first row of the adsorption tower is quickly depleted, and the adsorption / desorption cycle is shorter than that in other rows, resulting in a shortened service life and replacement cycle. Summary of the invention

[0005] 1. Technical issues to be resolved

[0006] One problem to be solved by the present disclosure is to provide an adsorption tower for controlling the fluid supply paths of multiple beds and a driving control method thereof. According to the adsorption value of each section, the fluid supply sequence is controlled differently, thereby improving the adsorption efficiency, reducing the overall operating cost of the adsorption tower, and extending the life of multiple beds. Therefore, it can be beneficial to environmental protection and can produce clean energy.

[0007] The problems to be solved by the present disclosure are not limited to the above-mentioned problems, and those skilled in the art can clearly understand other problems not mentioned from the following description.

[0008] (II) Technical solution

[0009] An adsorption tower for controlling the fluid supply path of multiple beds according to the present disclosure includes multiple beds arranged inside, a suction port connected to one side and a discharge port connected to the other side, and the adsorption tower may include: a sensor unit arranged inside each of the multiple beds; a plurality of connecting pipes connecting the suction port, the discharge port and at least two of the multiple beds; a plurality of valves connected to the multiple connecting pipes; a memory storing one or more instructions; and a processor executing the one or more instructions stored in the memory, and the processor may be configured to determine the fluid supply order of the multiple beds according to the adsorption values ​​within the multiple beds measured by the sensor unit, and open and close the valve according to the determined fluid supply order to control the fluid supply path.

[0010] According to the present disclosure, a driving control method for an adsorption tower that controls a fluid supply path of multiple beds is executed by a computing device, which includes a storage device that stores one or more instructions and a processor that executes the one or more instructions stored in the storage device. The driving control method for the adsorption tower may include: a step of measuring adsorption values ​​in a plurality of beds; a step of determining a fluid supply sequence for the plurality of beds based on the measured adsorption values ​​in the plurality of beds; and a step of opening and closing a valve to control the fluid supply path based on the determined fluid supply sequence.

[0011] Furthermore, in order to realize the present disclosure, a computer program stored in a computer-readable recording medium may be further provided.

[0012] Furthermore, in order to realize the present disclosure, a computer-readable recording medium recording the computer program may be further provided.

[0013] (III) Beneficial effects

[0014] According to an embodiment of the present disclosure, the adsorption value can be managed individually for each bed instead of centrally managing the adsorption tower. Therefore, the desorption cycle of the first and last rows of beds in the adsorption tower can be made consistent, thereby improving the adsorption efficiency.

[0015] In addition, according to an embodiment of the present disclosure, by making the adsorption cycle and the desorption cycle of the multiple beds consistent, the overall operating cost of the adsorption tower can be reduced and the life of the multiple beds can be extended.

[0016] The effects of the present disclosure are not limited to the above-mentioned effects, and other effects not mentioned can be clearly understood by those skilled in the art from the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a diagram briefly showing the configuration of an adsorption tower according to one embodiment of the present disclosure.

[0018] Figure 2is a configuration block diagram briefly showing the construction of an adsorption tower controlling fluid supply paths of a plurality of beds according to one embodiment of the present disclosure.

[0019] Figure 3 is a schematic diagram showing fluid flow of a first mode of an adsorption tower for controlling fluid supply paths of a plurality of beds according to one embodiment of the present disclosure.

[0020] Figure 4 is a schematic diagram illustrating fluid flow of a second mode of an adsorption tower for controlling fluid supply paths of a plurality of beds according to one embodiment of the present disclosure.

[0021] Figure 5 is a schematic diagram illustrating fluid flow in a third mode of an adsorption tower for controlling fluid supply paths of a plurality of beds according to one embodiment of the present disclosure.

[0022] Figure 6 is a flow chart of a driving control method of an adsorption tower for controlling fluid supply paths of a plurality of beds according to the present disclosure.

[0023] Figure 7 is a block diagram showing a configuration of a computing device according to one embodiment of the present disclosure. DETAILED DESCRIPTION

[0024] In the present disclosure, the same reference numerals refer to the same components. Not all elements of the embodiments are described in the present disclosure, and general contents of the technical field to which the present disclosure belongs or contents repeated between embodiments are omitted. The terms "unit, module, component, block" used in the specification may be implemented by software or hardware, and depending on the embodiment, multiple "units, modules, components, blocks" may be implemented by a single component, or one "unit, module, component, block" may include multiple components.

[0025] Throughout the specification, when one part is referred to as being “connected” to another part, this includes not only the case of being directly connected but also the case of being indirectly connected including being connected through a wireless communication network.

[0026] In addition, when a section is referred to as “comprising” a component, unless specifically stated otherwise, it means that other components may be further included, rather than excluding other components.

[0027] Throughout the specification, when a member is located “on” another member, this includes not only a case where one member is in contact with another member but also a case where other members exist between the two members.

[0028] Terms such as first, second, etc. are used to distinguish one component from another component, and the components are not limited by the above terms.

[0029] Unless the context clearly indicates otherwise, an expression in the singular includes an expression in the plural.

[0030] The reference numerals of the steps are used for convenience of description and do not describe the order of the steps. Unless the context clearly indicates a specific order, the steps may be performed in an order different from the specified order.

[0031] The working principle and embodiments of the present disclosure will be described below with reference to the accompanying drawings.

[0032] In this specification, "devices according to the present disclosure" include all kinds of devices that can perform computing and provide results to users. For example, the devices according to the present disclosure may include computers, server devices, and portable terminals, or any one of them.

[0033] Here, the computer may include, for example, a notebook computer equipped with a web browser, a desktop computer, a laptop computer, a tablet PC, a touch-screen tablet PC, etc.

[0034] The server device is a server that processes information by communicating with an external device, and may include an application server, a computing server, a database server, a file server, a game server, a mail server, a proxy server, a network server, etc.

[0035] The portable terminal is, for example, a wireless communication device that ensures portability and mobility, and may include all types of handheld wireless communication devices such as PCS (Personal Communication System), GSM (Global System for Mobile communications), PDC (Personal Digital Cellular), PHS (Personal Handyphone System), PDA (Personal Digital Assistant), IMT (International Mobile Telecommunication)-2000, CDMA (Code Division Multiple Access)-2000, W-CDMA (W-Code Division Multiple Access), WiBro (Wireless Broadband Internet), terminals, smart phones, and wearable devices such as watches, rings, bracelets, anklets, necklaces, glasses, contact lenses, or head-mounted devices (HMD).

[0036] The functions related to artificial intelligence according to the present disclosure are operated by a processor and a memory. The processor may be composed of one or more processors. At this time, one or more processors may be general-purpose processors such as a CPU (central processing unit), an AP or a DSP (digital signal processor), a graphics-specific processor such as a GPU (graphics processing unit) or a VPU (vision processing unit), or an artificial intelligence-specific processor such as an NPU (neural network processor). One or more processors control to process input data according to predefined operating rules or artificial intelligence models stored in the memory. Alternatively, when one or more processors are artificial intelligence-specific processors, the artificial intelligence-specific processors can be designed as a hardware structure specifically for processing a specific artificial intelligence model.

[0037] The characteristics of the predefined operating rules or artificial intelligence models are established through learning. Here, established through learning means that the basic artificial intelligence model is learned by a learning algorithm using a large amount of learning data, thereby establishing predefined operating rules or artificial intelligence models to perform the desired characteristics (or purposes). This learning can be performed in the device itself that executes the artificial intelligence according to the present disclosure, or it can be performed by a separate server and / or system. Examples of learning algorithms include supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but are not limited to the above examples.

[0038] The artificial intelligence model can be composed of multiple neural network layers. Each of the multiple neural network layers has multiple weight values, and the neural network operation is performed by the operation result of the previous layer and the operation between the multiple weight values. The multiple weight values ​​of the multiple neural network layers can be optimized according to the learning results of the artificial intelligence model. For example, during the learning process, multiple weight values ​​can be updated to reduce or minimize the loss value or cost value obtained from the artificial intelligence model. The artificial neural network may include a deep neural network (Deep Neural Network, DNN), such as a convolutional neural network (Convolutional Neural Network, CNN), a deep neural network (Deep Neural Network, DNN), a recurrent neural network (Recurrent Neural Network, RNN), a restricted Boltzmann machine (Restricted Boltzmann Machine, RBM), a deep belief network (Deep Belief Network, DBN), a bidirectional recursive deep neural network (Bidirectional Recurrent Deep Neural Network, BRDNN) or a deep Q network (Deep Q-Networks), etc., but not limited to the above examples.

[0039] According to an exemplary embodiment of the present disclosure, the processor can implement artificial intelligence. Artificial intelligence refers to a machine learning method based on an artificial neural network (ANN), which enables a machine to learn by imitating human biological neurons (biological neuron). According to the learning method, the artificial intelligence method can be divided into supervised learning, unsupervised learning, and reinforcement learning. The supervised learning is to provide input data and output data as learning data at the same time, so that the answer (output data) to the question (input data) has been determined. The unsupervised learning is to only provide input data without providing output data, so that the answer (output data) to the question (input data) has not been determined. The reinforcement learning is that whenever an action (Action) is taken in the current state (State), the external environment will give a reward (Reward) so that the learning proceeds in the direction of maximizing the reward. In addition, the artificial intelligence method can be distinguished according to the architecture of the structure of the learning model. The architecture of the widely used deep learning technology can be distinguished into convolutional neural networks (CNN), recursive neural networks (RNN), Transformer, generative adversarial networks (GAN), etc.

[0040] The present device and system may include an artificial intelligence model. The artificial intelligence model may be one artificial intelligence model or multiple artificial intelligence models. The artificial intelligence model may be composed of a neural network (or artificial neural network) and may include statistical learning algorithms that imitate biological neurons in machine learning and cognitive science. A neural network may refer to an overall model in which artificial neurons (nodes) that form a network through the combination of synapses change the binding strength of synapses through learning, and have problem-solving capabilities. Neurons in a neural network may include a combination of weight values ​​or biases. A neural network may include more than one layer consisting of one or more neurons or nodes. For example, a device may include an input layer, a hidden layer, and an output layer. The neural network that constitutes the device can change the weight values ​​of neurons through learning, so that the result to be predicted (output) can be inferred from any input (input).

[0041] The processor may generate a neural network, train or learn a neural network, perform operations based on received input data, generate information signals based on execution results, or retrain a neural network. The neural network model may include various types of models such as CNN (convolutional neural network) such as GoogleNet, AlexNet, VGG network, R-CNN (Region with Convolution 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 (Restrcted Boltzman Machine), Fully Convolutional Network, LSTM (Long Short-Term Memory) network, Classification Network, etc., but is not limited thereto. The processor may include more than one processor for performing operations according to a model of a neural network. For example, the neural network may include a deep neural network.

[0042] 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) and 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), etc. The present invention relates to a neural network comprising: a neural network (CNN), a deep neural network (ANN), a deep neural network (DLN), a deep neural network (DNC ...

[0043] According to an exemplary embodiment of the present disclosure, the processor can use CNN (Convolutional Neural Network) such as GoogleNet, AlexNet, VGG network, R-CNN (Region with Convolution 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 (Restrcted Boltzman Machine), Fully Convolutional Network, LSTM (Long Short-Term Memory) Network, Classification Network, Generative Modeling, Explainable AI, Continuous AI, Representation Learning. Learning), Material Design AI, BERT for Natural Language Processing, SP-BERT, MRC / QA, Text Analysis, Dialog System, GPT-3, GPT-4, Visual Analytics for Visual Processing, Visual Understanding, Video Synthesis, Anomaly Detection for ResNet Data Intelligence, Prediction, Time-Series Forecasting, Optimization, Recommendation, and Data Creation, but not limited thereto. Below, the embodiments of the present disclosure will be described in detail with reference to the accompanying drawings.

[0044] Figure 1 is a diagram briefly showing the structure of an adsorption tower 100 according to an embodiment of the present disclosure. Figures 2 to 6The adsorption tower 100 for controlling fluid supply paths of a plurality of beds and a method for controlling the same are described in detail.

[0045] like Figure 1 As shown, the adsorption tower 100 according to the present disclosure may include a plurality of beds T1, T2, T3 disposed therein, a suction port 30 connected to one side, and a discharge port 40 connected to the other side. A feed (Feed) containing ammonia, nitrogen, and hydrogen may be supplied through the suction port 30, ammonia may be adsorbed through an adsorption process, and a product (Product) containing nitrogen and hydrogen may be discharged from the discharge port 40.

[0046] In other words, the adsorption tower 100 receives air (feed) containing ammonia, volatile organic compounds (VOC), etc., and in the process of passing through the first bed T1 to the last bed T3, the air (product) from which ammonia, volatile organic compounds (VOC), etc. can be removed by applying steam recovery methods such as recompression, condensation, and activated carbon adsorption.

[0047] For example, in order to regenerate the used adsorption tower 100, a desorption gas may be introduced into the adsorption tower 100 to desorb ammonia inside the adsorption tower 100, and then part or all of the desorption gas may be discharged together with the desorbed ammonia. Alternatively, ammonia may be desorbed and removed using vacuum or heat without using a desorption gas.

[0048] Therefore, although not shown in the drawings, a separate exhaust pipe may be connected to each bed T1, T2, T3 to discharge ammonia desorbed by the desorption process in each bed T1, T2, T3 and part or all of the desorbed gas or ammonia by-product to the outside.

[0049] The region of the adsorption tower 100 from the position near the suction port 30 for receiving the feed to the position of the discharge port 40 for discharging the product may be divided into a first bed T1, a second bed T2, a third bed T3, etc., and a sensor unit 110 may be provided in each of the divided beds. The number of beds may be changed, and the sensor unit 110 may include at least one of a pressure sensor for measuring the internal pressure of the adsorption tower 100, a temperature sensor for measuring the internal temperature of the adsorption tower 100, a humidity sensor for measuring the internal humidity of the adsorption tower 100, a gas sensor for measuring the internal gas composition of the adsorption tower 100 to check the purity of the extracted hydrogen, and an adsorbent sensor.

[0050] When the ammonia concentration measured by the sensor unit 110 in the first bed T1 is greater than or equal to a preset value, or when the value measured in the first bed T1 and the value measured in the third bed T3 are greater than or equal to a preset value, the calculation device 120 (refer to Figure 2) determines that the adsorbent disposed in the first bed T1 has reached saturation and a desorption process can be performed.

[0051] Computing device 120 (see Figure 2 ) The sensor unit 110 can be used to confirm whether the pressure or temperature, etc. is maintained within a predetermined range to maintain the adsorption efficiency, and the humidity or hydrogen purity can be used to calculate the result value regarding the hydrogen extraction efficiency.

[0052] In addition, the adsorbent sensor of the sensor unit 110 can measure the state of the adsorbent inside the adsorption tower, evaluate the life and performance of the adsorbent, and obtain information that can predict the replacement time of the adsorbent.

[0053] Hereinafter, the gas supplied or discharged as feed or product will be collectively referred to as fluid.

[0054] In addition, the adsorption tower 100 may include a plurality of connecting pipes 31, 32, 33, 41, 42, 43 connecting the suction port 30, the discharge port 40 and at least two of the plurality of beds T1, T2, T3. The connecting pipes may include supply pipes 31, 32, 33 for supplying fluid and discharge pipes 41, 42, 43 for discharging fluid.

[0055] 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 may be connected.

[0056] Furthermore, a branch pipe 60 that branches to supply the fluid supplied from the suction port 30 to the supply pipes 31 , 32 , 33 connected to the respective beds T1 , T2 , T3 may be provided.

[0057] As an example, Figure 1 As shown, supply valves 50b, 50c can be set in the reverse supply pipes 32, 33 connected between the branch pipe 60 and the beds T2, T3, and the supply valve 50 can be omitted in the forward supply pipes 32, 33 executing the first mode, and the supply valve 50a can be set in the forward supply pipe 31 connected between the branch pipe 60 and the bed T1.

[0058] In one embodiment, supply valves 50 or discharge valves may be provided in all connecting pipes.

[0059] 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 may be connected.

[0060] On the other hand, the discharge pipe 41 discharged from the first bed T1 may be the supply pipe 32 for supplying fluid to the second bed T2, and the discharge pipe 42 discharged from the second bed T2 may be the supply pipe 33 for supplying fluid to the third bed T3. Figure 1 In the figure, the supply pipes 31, 32, 33 and the exhaust pipes 41, 42, 43 are drawn separately, but these may be connecting pipes that independently flow fluid in each mode, and the same connecting pipes may be shown separately according to the function.

[0061] In addition, although Figure 1 Only three beds are shown in the figure, but two or more beds may be provided depending on the adsorption process or situation.

[0062] like Figure 1 As shown, valves may be connected to each connecting pipe. The valves may include a supply valve 50 or a discharge valve (not shown).

[0063] When adsorption is performed in each bed, the supply valve 50 may be opened to supply a fluid such as a feed, and when desorption is performed as the adsorption value in each bed increases, the supply valve 50 and the discharge valve may be closed.

[0064] exist Figure 1 Although the supply valve 50 is shown only in a part of the supply pipe, supply valves 50a, 50b, 50c can be set in the supply pipes 31, 32, 33 connecting the beds T1, T2, T3 or in a part or all of the supply pipes connecting the suction port 30 and the beds T1, T2, T3.

[0065] although Figure 1 Although a discharge valve is not shown, a discharge valve may be provided in the discharge pipes 41, 42, 43 connecting the beds T1, T2, T3 or in part or all of the discharge pipes connecting the discharge port 40 and the beds T1, T2, T3.

[0066] like Figure 2 As shown, the adsorption tower 100 for controlling the fluid supply path of multiple beds according to the present disclosure may include: a sensor unit 110 for measuring the internal state of each bed T1, T2, and 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 driving unit 130 for driving the adsorption tower 100 according to an adsorption cycle and a desorption cycle set based on the internal state of the adsorption tower 100.

[0067] The sensor unit 110 may be disposed in each of a plurality of beds that are separated and partitioned inside the adsorption tower 100 .

[0068] The processor 14 and the memory 16 may be disposed together or separately in one or more computing devices 120. The computing device 120 may be used to control the driving of the adsorption tower 100 so that the efficiency of the adsorption tower 100 is maintained above a predetermined level.

[0069] The user interface 140 may receive parameters required for driving the adsorption tower 100 input by a user.

[0070] Additionally, the user interface 140 may provide the user with an alert when an abnormal condition occurs and receive information regarding the abnormal condition handling process.

[0071] The display unit 150 may provide an alarm for driving the adsorption tower 100, or may display the real-time operation status of the adsorption tower 100 for monitoring. The display unit 150 may display the value measured by the sensor unit 110, or may display the current desorption process progress, etc.

[0072] In addition, the adsorption tower 100 according to the present disclosure may further include an artificial intelligence model, which may be provided in the computing device 120 or may be connected through a separate server or cloud.

[0073] As an embodiment, the processor 14 can use the learned artificial intelligence model to output the corresponding adsorption cycle and desorption cycle according to the sensing data of the sensor unit 110. When the sensing data is within a preset optimal range, the instruction to drive the adsorption tower according to the outputted adsorption cycle and desorption cycle can be transmitted to the driving unit 130.

[0074] Alternatively, when the sensing data exceeds the optimal range, the processor 14 may transmit an instruction for executing an abnormal situation processing process according to a preset abnormal adsorption cycle and abnormal desorption cycle to the driving unit 130 .

[0075] According to the adsorption tower 100 of the present disclosure, based on the data collected by the sensor unit 110, the internal state of the bed at which the adsorption process cannot continue can be identified, and an artificial intelligence model that learns the normal state of the pressure, temperature, humidity, concentration, etc. of the gas in the adsorption tower 100 according to each time point of the adsorption process can be used to detect in real time the time point when the desorption process needs to be performed.

[0076] Thereby, the adsorption cycle and desorption cycle of each bed can be learned, and the adsorption and desorption of the adsorbent can be managed so that the adsorption cycle and desorption cycle of all beds are consistent.

[0077] The processor 14 can repeatedly self-diagnose at a preset cycle whether the sensing data measured by the sensor unit 110 exceeds the optimal range or whether the pattern of the previous sensing data and the pattern of the subsequent sensing data are different, and can use the artificial intelligence model to detect in real time when an abnormal state occurs.

[0078] In addition, when performing self-diagnosis, the processor 14 can not only detect abnormal conditions inside the adsorption tower 100, but also detect whether a sensor error occurs in at least one sensor of the sensor unit 110, or detect whether a communication error occurs between the sensor unit 110 and the processor 14. When a sensor error or a communication error is detected, an instruction to stop driving the adsorption tower 100 can be transmitted to the drive unit 130.

[0079] Generally speaking, from the first bed T1 to the third bed T3, the later the contact time with the feed is, the later the adsorbent replacement time occurs, so the adsorbent replacement time of each bed T1, T2, T3 will be different.

[0080] For example, the adsorbent in the first bed T1 may have reached the replacement time, while the adsorbent in the third bed T3 may still have sufficient remaining replacement time.

[0081] In the adsorption tower 100 according to the present disclosure, when each bed T1, T2, T3 has sufficient remaining replacement time, the following can be performed: Figure 3 The first mode is shown.

[0082] Alternatively, when the adsorption value of the first bed T1 decreases and the adsorption value of the adsorbent reaches saturation, it is possible to perform Figure 4 The second mode shown or Figure 5 The third mode is shown.

[0083] the following, Figures 3 to 5 The connecting pipe shown is a connecting pipe in which fluid flows, and the connecting pipe not shown is in a state in which fluid does not flow by blocking the supply valve 50 .

[0084] like Figure 3 As shown, when the adsorption value measured by the sensor unit 110 of each bed T1, T2, T3 is less than the preset threshold, the processor 14 of the adsorption tower 100 according to the present disclosure determines that the adsorbent has not reached saturation and the adsorption process can be performed, and the first mode can be executed.

[0085] In the first mode, forward adsorption may be performed in which the fluid flowing in from the suction port 30 passes through the first bed T1 , the second bed T2 , and the third bed T3 and is discharged from the discharge port 40 .

[0086] The processor 14 may close the supply valves 50b, 50c connecting the suction port 30 or the branch pipe 60 with the second bed T2 or the third bed T3, and may open the supply valve 50a connected to the first bed T1.

[0087] On the other hand, Figure 4 As shown, when the adsorption value in the first bed T1 measured by the sensor unit 110 of the first bed T1 exceeds a preset threshold, the processor 14 of the adsorption 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 the second mode can be executed.

[0088] In addition, when 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 judged that the adsorbent in the first bed T1 has reached saturation and the adsorption process cannot be carried out, and the second mode can be executed.

[0089] In the second mode, reverse adsorption may be performed in which the fluid flowing in from the suction port 30 passes through the third bed T3 , the second bed T2 , and the first bed T1 and is discharged from the discharge port 40 .

[0090] The processor 14 may control to close the supply valve 50a connected between the branch pipe 60 and the first bed T1 and to open the supply valve 50c connected between the branch pipe 60 and the third bed T3 to reversely supply the fluid from the third bed T3.

[0091] In other words, the forward direction is the direction from the lower part toward the upper part of the adsorption tower 100, and the reverse direction is the direction from the upper part toward the lower part, which is the reverse order of the sequentially arranged beds.

[0092] As an embodiment, the supply valve 50 b provided in the supply pipe 32 is closed together so that the fluid in the suction port 30 does not flow to the first bed T1 and the second bed T2 but directly flows to the third bed T3 .

[0093] Therefore, in the first mode, the adsorption process can be performed in the forward direction, and when the adsorption value of the adsorbent of the first bed T1 is high or differs greatly from the adsorption rate of the third bed T3, in the second mode, the adsorption process can be performed in the reverse direction to maintain the adsorption efficiency of all beds T1, T2, and T3 at a predetermined level and manage them effectively.

[0094] As another example, Figure 5 As shown, when the adsorption value in the first bed T1 measured by the sensor unit 110 of the first bed T1 exceeds a preset threshold, the processor 14 of the adsorption 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 the third mode can be executed.

[0095] In addition, when 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 judged that the adsorbent in the first bed T1 has reached saturation and the adsorption process cannot be carried out, and the third mode can be executed.

[0096] exist Figure 5 In the third mode shown in (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 inlet 30 or the branch pipe 60 and the third bed T3 is opened to reversely supply the fluid.

[0097] Or, in Figure 5 In the third mode shown in (b), the supply valve 50a connected between the inlet 30 or the branch pipe 60 and the first bed T1 is closed, and the supply valve 50b connected between the inlet 30 or the branch pipe 60 and the second bed T2 or the supply valve 50c connected between the inlet 30 or the branch pipe 60 and the third bed T3 is opened to supply fluid in a positive direction.

[0098] according to Figure 5 (a) and Figure 5 The control of (b) can be selected by further considering the adsorption value difference between the second bed T2 and the third bed T3. In other words, when the adsorption value difference exceeds the preset value, the processor 14 can execute the control according to Figure 5 When the adsorption value difference is less than or equal to the preset value, the processor 14 may execute the control according to Figure 5 (b) control.

[0099] Therefore, a desorption process can be performed in the first bed T1, and an adsorption process can be performed in the second bed T2 or the third bed T3. The inflow and outflow of the fluid can be blocked by closing the supply valve 50a or the discharge valve (not shown) of the first bed T1, and the fluid can be reversed or forwarded by opening the supply valves 50b, 50c or the discharge valves (not shown) of the second bed T2 and the third bed T3.

[0100] On the other hand, when the adsorption value measured by the sensor unit 110 of the first bed T1 is less than or equal to the threshold value again, the processor 14 of the adsorption tower 100 according to the present disclosure may open the supply valve 50a connected between the branch pipe 60 and the first bed T1. The processor 14 may control to sequentially perform the adsorption process in the plurality of beds T1, T2, T3 including the first bed T1. Return to Figure 3The first mode is to prevent an abnormal state caused by an error of the sensor unit 110.

[0101] As an example, different types of adsorbents may be provided in each of the plurality of beds T1, T2, and T3. 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. A cheap adsorbent with low adsorption efficiency may be used in the first bed T1 having a higher ammonia concentration, and an expensive adsorbent with high adsorption efficiency may be used in the second bed T2 and the third bed T3 having a lower ammonia concentration.

[0102] As an example, the type of adsorbent may be determined by considering the temperature condition of the upper or lower portion of the adsorption tower 100, or the beds may be arranged in the order of the adsorbent having the lower desorption temperature.

[0103] Therefore, the entire bed can be divided into a plurality of beds T1, T2, T3, and a sensor unit 110 is provided for each bed to uniformly manage the adsorbent efficiency of the upper and lower parts of the adsorption tower 100 and perform the adsorption process at the best efficiency. At this time, the best efficiency refers to a state in which the adsorption efficiency is maintained above a certain level while delaying the replacement time of the adsorbent to the maximum extent to reduce the cost.

[0104] Therefore, the adsorption tower 100 according to the present disclosure may be driven to recognize the internal state of each divided portion inside the adsorption tower 100 and perform separate adsorption and desorption processes accordingly.

[0105] As an embodiment, when it is detected that the temperature, humidity or pressure range is exceeded by the artificial intelligence model according to the current amount of adsorbent and the internal process of the adsorption tower 100, an alarm indicating the need to change from the first mode to the second mode or the third mode can be provided through the display unit 150.

[0106] The adsorption tower 100 including a plurality of beds may divide one adsorption tower 100 into a plurality of sections, or may combine a plurality of adsorption towers 100 in series to finally separate spaces where respective adsorbents are disposed.

[0107] In addition, the adsorption tower 100 according to the present disclosure can use an artificial intelligence model to determine the feed supply sequence between multiple beds T1, T2, and T3 and generate a new feed supply path that excludes the bed that performs regeneration at a predicted time point when the adsorbent regeneration cycle including the desorption process enters.

[0108] Specifically, when the feed is supplied from the suction port 30 of the adsorption tower 100 and flows out from the discharge port 40, a pressure drop phenomenon occurs in which the pressure of the suction port 30 is higher than the pressure of the discharge port 40. In other words, the pressure of the suction port 30 must be higher than the pressure of the discharge port 40 to form a normal fluid flow, and for this reason, preferably, the difference between the pressure P1 of the suction port 30 and the pressure P2 of the discharge port 40 is kept constant.

[0109] However, when the flow rate becomes faster or the flow rate increases according to the internal conditions of the adsorption tower 100, the gap between P1 and P2 increases, causing the P1-P2 value to continue to increase. Considering that the size of P1 cannot be increased indefinitely, the gap between P1 and P2 needs to be controlled.

[0110] Therefore, when an internal situation of increased flow rate or increased flow rate occurs, the adsorption tower 100 according to the present disclosure can issue a real-time dynamic structural change instruction based on the artificial intelligence model, such as additionally connecting multiple beds, or connecting beds connected in series in parallel, etc. By controlling the flow rate and flow rate of the fluid through real-time dynamic structural changes of multiple beds T1, T2, and T3, the P1-P2 value can be prevented from increasing, and the fluid can be stably supplied without increasing the value of P1.

[0111] As an embodiment, the artificial intelligence model may monitor the status of each bed in real time through the sensor unit 110 disposed in the multiple beds T1, T2, and T3.

[0112] In addition, as an embodiment, the artificial intelligence model can determine whether the flow rate or flow velocity in the bed is greater than or equal to a threshold flow rate or flow velocity based on the flow rate value or flow velocity value of the fluid acquired by the sensor unit 110. At this time, the sensor unit 110 can include a flow sensor or a flow velocity sensor.

[0113] As an example, when the flow rate or flow velocity within the bed is greater than or equal to a threshold flow rate or flow velocity, the artificial intelligence model may convert the configuration of the multiple beds T1, T2, T3 from a series connection to a parallel connection.

[0114] Alternatively, as an embodiment, when the flow rate or flow velocity within the bed is greater than or equal to a threshold flow rate or flow velocity, or when the pressure difference between the suction port and the discharge port of the adsorption tower is greater than or equal to a threshold, the artificial intelligence model can connect the additional bed and determine a fluid supply path including the additional bed and excluding the bed that needs to be regenerated. Among multiple schemes for controlling to reduce the flow rate or flow velocity, the artificial intelligence model can determine the final scheme based on the internal conditions of the adsorption tower 100, taking into account the resources required for time, cost, etc. In this way, the flow rate or flow velocity within multiple beds T1, T2, and T3 can be controlled.

[0115] On the other hand, the driving control method of the adsorption tower 100 according to the present disclosure may be executed by a computing device 120 , wherein the computing device 120 includes a memory 16 storing one or more instructions and a processor 14 executing the one or more instructions stored in the memory 16 .

[0116] like Figure 6 As shown, the driving control method of the adsorption tower 100 according to the present disclosure includes: a step of measuring adsorption values ​​in a plurality of beds T1, T2, T3 (S610); a step of determining the fluid supply sequence of the plurality of beds T1, T2, T3 according to the measured adsorption values ​​in the plurality of beds T1, T2, T3 (S620); and a step of opening and closing the valve to control the fluid supply path according to the determined fluid supply sequence (S630).

[0117] In step S620 , when the adsorption value measured by the sensor unit 110 of each bed T1 , T2 , T3 is less than a preset threshold, a forward adsorption sequence passing through the first bed T1 , the second bed T2 , the third bed T3 and being discharged from the discharge port 40 may be determined.

[0118] In step S630 , the first mode may be performed to control the supply valve 50 or the discharge valve so that the fluid flowing in from the suction port 30 passes through the first bed T1 , the second bed T2 , and the third bed T3 and is discharged from the discharge port 40 .

[0119] Specifically, the supply valves 50b, 50c connecting the suction port 30 or the branch pipe 60 with the second bed T2 or the third bed T3 may be closed, and the supply valve 50a connected to the first bed T1 may be opened.

[0120] On the other hand, in step S620, when the adsorption value within the first bed T1 measured by the sensor unit 110 of the first bed T1 exceeds a preset threshold, a reverse adsorption sequence passing through the third bed T3, the second bed T2, the first bed T1 and discharged from the discharge port 40 can be determined.

[0121] Alternatively, in step S620, when 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 T3 in the third bed T3 measured by the sensor unit 110 of the third bed T3 exceeds a preset threshold, a reverse adsorption order passing through the third bed T3, the second bed T2, the first bed T1 and being discharged from the discharge port 40 can be determined.

[0122] In step S630 , the second mode may be performed to control the supply valve 50 or the discharge valve so that the fluid flowing in from the suction port 30 passes through the third bed T3 , the second bed T2 , and the first bed T1 and is discharged from the discharge port 40 .

[0123] Specifically, control may be performed to close the supply valve 50a connected between the branch pipe 60 and the first bed T1 and to open the supply valve 50c connected between the branch pipe 60 and the third bed T3 to reversely supply the fluid from the third bed T3.

[0124] Alternatively, in step S620, it may be determined that the first bed T1 having an adsorption value exceeded is completely discharged from the adsorption process and a desorption process is performed thereon.

[0125] In step S630, the third mode can be executed, closing the supply valve 50a connected between the suction port 30 or the branch pipe 60 and the first bed T1, opening 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 reverse or forward direction.

[0126] For the sake of brevity, the content that repeats the above content will be omitted.

[0127] On the other hand, the adsorption value of each bed T1, T2, and T3 can be confirmed by using the temperature sensor of the sensor unit 110. The more active the adsorption, the higher the temperature, the lower the adsorption efficiency, and the closer the adsorbent replacement time is, the lower the temperature, so the change in the adsorbent state can be seen from the change in temperature. When the temperature drops, it can be judged that the adsorption value exceeds the preset threshold, and when the temperature remains high, it can be judged that the adsorption value of the adsorbent is less than or equal to the preset threshold.

[0128] on the other hand, Figure 7 A computing device according to an embodiment of the present disclosure is shown. Figure 7 As shown, the drive control method of the adsorption tower 100 for controlling the fluid supply path of multiple beds according to the present disclosure can be implemented 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 the instructions are executed by a processor, a program module can be generated to perform the operations of the disclosed embodiments. The recording medium can be implemented in the form of a computer-readable recording medium.

[0129] The computing device 120 of the adsorption tower 100 according to the present disclosure may correspond to the computing device 12, which may include at least one processor 14, a computer-readable storage medium 16 including a program 20, and a communication bus 18. In addition, the computing device 12 may further include one or more input / output interfaces 22 and one or more network communication interfaces 26 that provide interfaces for the input / output device 24.

[0130] The user interface 140 and the display unit 150 of the present disclosure may correspond to more than one input / output interface 22 that provides an interface for the input / output device 24. Therefore, each component of the adsorption tower drive control device for controlling fluid flow by segment according to the present disclosure may be fully included in the above-mentioned one computing device 12 or each component may be implemented in a separate device.

[0131] The user interface 140 and the display unit 150 of the present disclosure may correspond to one or more input / output interfaces 22 that provide interfaces for the input / output device 24. Therefore, each component of the adsorption tower 100 for controlling fluid supply paths of multiple beds according to the present disclosure may be fully included in the above-mentioned one computing device 12 or each component may be implemented in a separate device.

[0132] As described above, the disclosed embodiments are described with reference to the accompanying drawings. Those skilled in the art to which the present disclosure belongs will understand that the present disclosure can be implemented in a form different from the disclosed embodiments without changing the technical ideas or essential features of the present disclosure. The disclosed embodiments are exemplary and should not be interpreted as limiting.

Claims

1. An adsorption tower for controlling a fluid supply path of a plurality of beds, comprising a plurality of beds arranged inside, a suction port connected to one side and a discharge port connected to the other side, the adsorption tower comprising: a sensor unit disposed inside each of the plurality of beds; a plurality of connecting pipes connecting the suction port, the discharge port and at least two of the plurality of beds; a plurality of valves connected to the plurality of connecting pipes; a memory storing one or more instructions; as well as a processor, executing the one or more instructions stored in the memory, The processor is configured to determine a fluid supply sequence for the plurality of beds according to adsorption values ​​within the plurality of beds measured by the sensor unit, and to open and close the valve to control a fluid supply path according to the determined fluid supply sequence.

2. The adsorption tower for controlling fluid supply paths of a plurality of beds according to claim 1, wherein: The connecting pipe includes branch pipes, and the branch pipes are respectively connected to the suction port and at least one of the plurality of beds. The valve includes a supply valve connected between the branch pipe and at least one bed among the plurality of beds.

3. The adsorption tower for controlling fluid supply paths of a plurality of beds according to claim 2, wherein: The plurality of beds include at least a first bed arranged in a first row and a second bed arranged in a last row, The processor is configured to: When the adsorption value in the first bed measured by the sensor unit exceeds a preset threshold, control is performed to close the supply valve connected between the branch pipe and the first bed, and to open the supply valve connected between the branch pipe and the second bed to reversely supply fluid from the second bed, The reverse direction is the order opposite to the order in which the beds are arranged sequentially.

4. The adsorption tower for controlling fluid supply paths of a plurality of beds according to claim 2, wherein: The plurality of beds include at least a first bed arranged in a first row and a second bed arranged in a last row, The processor is configured to: When the difference in fluid concentration between the first bed and the second bed measured by the sensor unit exceeds a preset threshold, control is performed to close the supply valve connected between the branch pipe and the first bed, and to open the supply valve connected between the branch pipe and the second bed to reversely supply fluid from the second bed, The reverse direction is the order opposite to the order in which the beds are arranged sequentially.

5. The adsorption tower for controlling fluid supply paths of a plurality of beds according to claim 2, wherein: The plurality of beds include at least a first bed, a second bed and a third bed arranged in sequence, The processor is configured to: When the adsorption value in the first bed measured by the sensor unit exceeds a preset threshold, control is performed to close the supply valve connected to the first bed, and to open the supply valve connected between the branch pipe and the second bed or the third bed to supply fluid in a forward or reverse direction, The forward direction is the order in which the beds are arranged sequentially, and the reverse direction is the order opposite to the forward direction.

6. The adsorption tower for controlling fluid supply paths of a plurality of beds according to claim 5, wherein: The processor performs control to perform a desorption process in the first bed and an adsorption process in the second bed or the third bed.

7. The adsorption tower for controlling fluid supply paths of a plurality of beds according to claim 6, wherein: The processor is configured to: When the adsorption value measured by the sensor unit of the first bed is less than or equal to a threshold value, control is performed to open a supply valve connected between the branch pipe and the first bed, and an adsorption process is sequentially performed in a plurality of beds including the first bed.

8. The adsorption tower for controlling fluid supply paths of a plurality of beds according to claim 7, wherein: The plurality of beds include at least a first bed, a second bed and a third bed arranged in sequence, A different type of adsorbent is disposed in each of the plurality of beds, 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 adsorption tower for controlling fluid supply paths of a plurality of beds according to claim 8, wherein: The sensor unit includes at least one sensor among a pressure sensor, a temperature sensor, a humidity sensor, a gas sensor, and an adsorbent sensor, The processor is configured to transmit an instruction to stop driving the adsorption tower to the driving unit when an error is detected in the at least one sensor or a communication error is detected between the at least one sensor and the processor.

10. The adsorption tower for controlling fluid supply paths of a plurality of beds according to claim 1, wherein: The processor is configured to determine the fluid supply sequence or fluid supply path using an artificial intelligence model when the flow rate or flow velocity within the bed is greater than or equal to a preset threshold, or the pressure difference between the suction port and the discharge port of the adsorption tower is greater than or equal to a preset threshold, and transmit instructions to change the connection structure of the multiple beds based on the determined fluid supply sequence or fluid supply path to control the pressure between the suction port and the discharge port.

11. A method for driving and controlling an adsorption tower for controlling a fluid supply path of a plurality of beds, the method being executed by a computing device, the computing device comprising a storage storing one or more instructions and a processor executing the one or more instructions stored in the storage, the method for driving and controlling the adsorption tower comprising: Steps for measuring adsorption values ​​within a plurality of beds; the step of determining a fluid supply sequence for the plurality of beds based on measured adsorption values ​​within the plurality of beds; as well as The step of opening and closing the valves to control the fluid supply paths according to the determined fluid supply sequence.

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