Digital twin-based water treatment plant operation management system

Abstract

A digital twin-based water treatment plant operation management system of the present invention may comprise: a data collection / storage unit for collecting and storing, in real time, data including the operation state and an operation factor of a raw water treatment process in a water treatment facility; a water quality prediction unit for predicting the quality of purified water resulting from the raw water treatment process in the water treatment facility by using the operation factor of the raw water treatment process in the water treatment facility; an abnormality detection unit for detecting signs of abnormalities in the raw water treatment process in the water treatment facility when the predicted quality of the purified water exceeds a pre-set water quality standard; an optimal factor deriving unit for deriving, in a case where signs of abnormalities in the raw water treatment process are detected or there is a change in the raw water characteristics from the current operation state of the raw water treatment process, a raw water treatment process operation factor generated when controlling the operation state of the water treatment facility; and a digital twin system unit which constructs a digital twin 3D model in which the water treatment facility is reproduced in a virtual 3D graphic by applying a 3D asset of the water treatment facility, structures the 3D asset so that the 3D asset has attributes of a driving unit, which is a set of dynamic information for reflecting, in the virtual space of the digital twin 3D model, the physical behavior of the water treatment facility in the digital twin 3D model, and an information unit, which is a set of static information, and includes a linkage unit for performing the role of an interface for collecting data and providing information together with the driving unit and the information unit.

Description

Digital Twin-based Water Treatment Plant Operation Management System

[0001] The present invention relates to a digital twin-based water purification plant operation and management system, and more specifically, to a digital twin-based water purification plant operation and management system capable of performing real-time monitoring, artificial intelligence water quality prediction, process diagnosis, and management of a water purification plant based on a digital twin model.

[0002] Water supply facilities are a series of facilities that purify raw water supplied from water sources at geographically separated unit sites through a water purification process and then distribute it to the general public via transmission and distribution pipelines.

[0003] Conventional water supply operation systems are configured as systems that collect real-time data from control equipment installed in water purification facilities and monitor and control it, with a manager in a central control room remotely controlling the system through on-site monitoring and control equipment and central monitoring and control equipment.

[0004] However, conventional water supply operating systems relied on real-time remote monitoring and control of water treatment facilities by a central control room or operations manager, which meant that water purification plants had to be operated based on techniques of post-maintenance management.

[0005] Therefore, the maintenance response direction for water treatment plants needs to move away from the existing concept of reactive maintenance and introduce techniques based on a proactive, preventive maintenance concept.

[0006] Accordingly, the present invention has been devised to solve the aforementioned problems, and the objective of the present invention is to provide a digital twin-based water purification plant operation management system capable of performing real-time monitoring, artificial intelligence water quality prediction, process diagnosis, and management of the water purification plant based on a digital twin model for the proactive maintenance of water purification facilities.

[0007] In addition, the objective of the present invention is to provide a digital twin-based water purification plant operation management system equipped with a digital twin system unit that constructs a digital twin 3D model by applying a 3D asset of a water purification facility, structures the 3D asset to have the attributes of a driving unit, which is a set of dynamic information for reflecting the physical behavior of the water purification facility in the virtual space of the digital twin 3D model, and an information unit, which is a set of static information, and a linkage unit that performs the role of an interface for data collection and information provision together with the driving unit and the information unit.

[0008] However, the technical problems to be solved by the present invention are not limited to those mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art to which the present invention belongs from the description below.

[0009] A digital twin-based water purification plant operation management system according to an embodiment of the present invention for achieving the above-mentioned purpose comprises: a data / collection storage unit that collects and stores data including raw water treatment process operation factors and operating status of a water purification facility in real time; a water quality prediction unit that predicts the water quality of purified water according to the raw water treatment process of the water purification facility using the raw water treatment process operation factors of the water purification facility based on an algorithm of an artificial intelligence model; an anomaly detection unit that detects signs of an anomaly in the raw water treatment process of the water purification facility when the water quality of the purified water predicted by the water quality prediction unit exceeds a preset water quality standard based on an algorithm of an artificial intelligence model; and an optimal factor derivation unit that derives raw water treatment process operation factors generated when controlling the operating state of the water purification facility based on an algorithm of an artificial intelligence model when an anomaly in the raw water treatment process is detected by the anomaly detection unit, or when a change in the raw water characteristics and the current operating status of the raw water treatment process occurs. The digital twin system may include a digital twin 3D model that reproduces the water treatment facility as a virtual 3D graphic by applying the 3D asset of the water treatment facility, and the 3D asset is structured to have the attributes of a driving unit, which is a set of dynamic information for reflecting the physical behavior of the water treatment facility in the virtual space of the digital twin 3D model, and an information unit, which is a set of static information, and a linkage unit that performs the role of an interface for data collection and information provision together with the driving unit and the information unit.

[0010] In addition, a digital twin-based water purification plant operation management system according to one embodiment of the present invention may further include a comprehensive analysis unit that performs a simulation of a scenario assuming that the raw water treatment process operation factor is applied to the raw water treatment process, and derives a water quality prediction result that predicts the water quality of the raw water treatment process based on the simulation.

[0011] And the above raw water treatment process operating parameter may be a setting value of at least one of the raw water treatment process, power consumption, chemical input amount, agitator rotation speed, filtration time, backwashing frequency, and water quantity, for ensuring that the water quality of the purified water complies with the above preset water quality.

[0012] In addition, the digital twin system can display the water quality prediction results in the virtual space of the digital twin 3D model.

[0013] And, a digital twin-based water purification plant operation management system according to one embodiment of the present invention may further include a monitoring unit that monitors the raw water treatment process operation factors and operation status of the water purification facility stored in real time in the data collection / storage unit.

[0014] In addition, the operating status of the above water treatment facility may include whether the process facility, which is a subsystem of the facilities constituting the above water treatment facility, and the equipment, which is a subsystem of the above process facility, are in operation.

[0015] And the digital twin system unit can display the raw water treatment process operation parameters and operation status of the water purification facility in the virtual space of the digital twin 3D model.

[0016] In addition, a digital twin-based water purification plant operation management system according to one embodiment of the present invention may further include a facility information unit that transmits the facility information to the digital twin system unit so that the facility information is displayed in the virtual space of the digital twin 3D model.

[0017] And the above facility information may be data including the specifications, drawings, and photographs of the facilities constituting the water purification facility, the process facilities which are subsystems of the facility, and the equipment and auxiliary equipment which are subsystems of the process facilities, as well as the maintenance history and CCTV surveillance footage of the facilities, process facilities, equipment and auxiliary equipment.

[0018] In addition, the digital twin system can display the facility information in the virtual space of the digital twin 3D model.

[0019] And the digital twin system unit applies 3D assets of the water purification facility, process facility, equipment, and auxiliary equipment to extract 3D asset models of the facility, process facility, equipment, and auxiliary equipment, respectively, and then structures an asset hierarchy such that the facility 3D asset model is a higher-level system of the process facility 3D asset model, and the process facility 3D asset model is a higher-level system of the equipment 3D asset model and auxiliary equipment 3D asset model, thereby systematizing the relationships between the 3D asset models, and based on the structure of the asset hierarchy, the facility, process facility, equipment, and auxiliary equipment 3D asset models can be displayed in the virtual space of the digital twin 3D model.

[0020] In addition, the above process facility may be a raw water treatment process including a mixing tank, a coagulation sedimentation tank, a sedimentation / filtration tank, and a purified water tank for carrying out the above raw water treatment process.

[0021] And the above equipment may be a pump, valve, or motor operated to carry out the above raw water treatment process.

[0022] In addition, the above auxiliary equipment may be a platform or fence installed on the facility.

[0023] And the digital twin system unit can present the digital twin 3D model to the user in the form of a user interface.

[0024] The present invention enables real-time monitoring, artificial intelligence water quality prediction, process diagnosis, and management of water purification facilities by constructing a digital twin 3D model, thereby enabling proactive maintenance of water purification facilities.

[0025] In addition, the present invention can provide a digital twin 3D model in which a 3D asset is structured with a driving unit for reflecting the physical behavior of a water purification facility in the virtual space of the digital twin 3D model and an information unit composed of information about the driving unit.

[0026] However, the effects obtainable from the present invention are not limited to those mentioned above, and other unmentioned effects will be clearly understood by those skilled in the art from the description below.

[0027] FIG. 1 is a block diagram schematically illustrating the components of a digital twin-based water purification plant operation management system according to one embodiment of the present invention.

[0028] FIG. 2 is a flowchart illustrating the process of an AI solution for learning, evaluating, and verifying an artificial intelligence model configured in a water quality prediction unit, an anomaly detection unit, and an optimal factor derivation unit according to one embodiment of the present invention.

[0029] FIG. 3 is a drawing for explaining the 3D asset structuring of a water purification facility according to one embodiment of the present invention.

[0030] FIG. 4 is a block diagram illustrating an example of a process facility according to one embodiment of the present invention.

[0031] FIG. 5 is a block diagram illustrating an example of a 3D asset model of a water purification facility according to one embodiment of the present invention.

[0032] FIG. 6 is a drawing illustrating an example of a 3D asset model of a valve, which is a facility according to one embodiment of the present invention.

[0033] FIG. 7 is a drawing illustrating an example of a 3D asset model of a motor, which is a piece of equipment according to one embodiment of the present invention.

[0034] FIG. 8 is a diagram illustrating a method of providing a digital twin 3D model according to an embodiment of the present invention.

[0035] FIG. 9 is a schematic diagram illustrating the configuration of a digital twin system unit according to one embodiment of the present invention.

[0036] Hereinafter, embodiments of the present invention are described in detail with reference to the attached drawings so that those skilled in the art can easily implement the present invention. However, since the description of the present invention is merely an example for structural or functional explanation, the scope of the present invention should not be interpreted as being limited by the embodiments described in the text. That is, since the embodiments are subject to various modifications and may take various forms, the scope of the present invention should be understood to include equivalents capable of realizing the technical concept. Furthermore, the objectives or effects presented in the present invention do not imply that a specific embodiment must include all of them or only such effects; therefore, the scope of the present invention should not be understood as being limited by them.

[0037] The meaning of the terms described in this invention should be understood as follows.

[0038] Terms such as "first" and "second" are intended to distinguish one component from another, and the scope of rights shall not be limited by these terms. For example, the first component may be named the second component, and similarly, the second component may be named the first component. When a component is referred to as being "connected" to another component, it should be understood that it may be directly connected to that other component, or that there may be other components in between. Conversely, when a component is referred to as being "directly connected" to another component, it should be understood that there are no other components in between. Meanwhile, other expressions describing the relationship between components, such as "between" and "exactly between," or "adjacent to" and "directly adjacent to," shall be interpreted in the same manner.

[0039] A singular expression should be understood to include a plural expression unless the context clearly indicates otherwise, and terms such as "include" or "have" are intended to specify the existence of the set-up features, numbers, steps, actions, components, parts, or combinations thereof, and should be understood not to preclude the existence or addition of one or more other features, numbers, steps, actions, components, parts, or combinations thereof.

[0040] Unless otherwise defined, all terms used herein have the same meaning as generally understood by those skilled in the art to which this invention pertains. Terms defined in commonly used dictionaries should be interpreted as having meanings consistent with the context of the relevant technology and should not be interpreted as having an ideal or overly formal meaning unless explicitly defined in this invention.

[0041] Water purification plant operation management system

[0042] Hereinafter, the configuration of a preferred embodiment of the digital twin-based water purification plant operation management system (100) of the present invention will be described in detail with reference to the attached drawings.

[0043] The digital twin-based water purification plant operation management system (100) of the present invention is a system for performing real-time monitoring, artificial intelligence water quality prediction, process diagnosis, and management of water purification facilities based on a digital twin model, and for this purpose, the components shown in FIG. 1 may be provided.

[0044] * FIG. 1 is a block diagram schematically illustrating the components of a digital twin-based water purification plant operation management system according to one embodiment of the present invention.

[0045] Referring to FIG. 1, the water purification plant operation management system (100) of the present invention may be equipped with a data collection / storage unit (110), a water quality prediction unit (120), an anomaly detection unit (130), an optimal factor derivation unit (140), a comprehensive analysis unit (150), a monitoring unit (160), a facility information unit (170), and a digital twin system unit (180).

[0046] In one embodiment, the data collection / storage unit (110) can collect and store data including raw water treatment process operation factors and operation status of the water purification facility (10) in real time.

[0047] At this time, the data collection / storage unit (110) can collect and store data including raw water treatment process operation factors and operation status of the water treatment facility (10) in conjunction with the HMI or SCADA system of the water treatment facility (10).

[0048] Additionally, the operating factors of the raw water treatment process of the water purification facility (10) are not limited, but in one embodiment, they refer to the operating factors of the process facility (12) which is a subsystem of the facility (11) constituting the water purification facility (10).

[0049] Here, the raw water treatment process operation parameters may include information values ​​regarding the quality of the raw water, such as the flow rate of the raw water flowing into the water purification facility (10), turbidity, pH, and water temperature.

[0050] In addition, the raw water treatment process operating parameter may be at least one of the set values ​​of the raw water treatment process, such as the power consumption of the raw water treatment process, the amount of chemicals injected, the rotation speed of the agitator, the filtration time, the number of backwashing cycles, and the amount of water.

[0051] That is, the raw water treatment process operation parameters stored in the data collection / storage unit (110) may include information values ​​of the raw water and setting values ​​of the raw water treatment process.

[0052] And the operating state of the water purification facility (10) is not limited, but in one embodiment, it may include the operating state of the process facility (12) which is a subsystem of the facility (11) constituting the water purification facility (10) and the equipment (13) which is a subsystem of the process facility (12).

[0053] In one embodiment, the water quality prediction unit (120) can predict the water quality of the water purification facility (10) according to the raw water treatment process of the water purification facility (10) by using the raw water treatment process operation factor of the water purification facility (10) based on the algorithm of the artificial intelligence model.

[0054] At this time, the artificial intelligence model of the water quality prediction unit (120) is not limited in type, but in one embodiment, the artificial intelligence model may be a regression analysis model for predicting the water quality of purified water.

[0055] Additionally, the algorithm of the regression analysis model configured in the water quality prediction unit (120) is not limited in type, but in one embodiment, the algorithm of the regression analysis model may be a regression analysis algorithm, and the regression analysis algorithm used by the regression analysis model of the water quality prediction unit (120) to predict the water quality of purified water may be at least one of XGBoost, LightGBM, CatBoost, and Auto Arima.

[0056] The regression analysis algorithms described above in this invention, XGBoost, LightGBM, CatBoost, and Auto Arima, are commonly disclosed algorithms, so a detailed explanation thereof will be omitted for convenience.

[0057] In one embodiment, the water quality prediction unit (120) may predict the water quality of the purified water according to the raw water treatment process of the water treatment facility (10) based on external conditions at the time when raw water flows into the water treatment facility (10).

[0058] Here, external conditions may include the season, rainfall, day of the week (weekday / weekend), external temperature, and whether algae bloom occurs at the time when raw water flows into the water purification facility (10).

[0059] That is, the water quality prediction unit (120) can predict the water quality of the water purification facility (10) according to the raw water treatment process of the water purification facility (10) by using the raw water treatment process operation factors and external conditions based on the algorithm of the regression analysis model.

[0060] Additionally, although not shown in the drawing, the water purification plant operation management system (100) is preferably equipped with a measuring means (not shown), such as a sensor, that measures external conditions and transmits them to the water quality prediction unit (120) so that the water quality prediction unit (120) can predict the water quality of purified water based on external conditions.

[0061] In one embodiment, the anomaly detection unit (130) can detect an anomaly in the raw water treatment process of the water purification facility (10) when the water quality of the purified water predicted by the water quality prediction unit (120) exceeds a preset water quality standard based on the algorithm of the artificial intelligence model.

[0062] Here, the water quality of purified water can be determined based on data such as turbidity, pH, and residual chlorine concentration.

[0063] In addition, the pre-set water quality refers to the water quality that can be supplied from the water treatment facility (10) to consumers after the raw water flowing into the water treatment facility (10) is converted into purified water through a treatment process.

[0064] And it is desirable for the abnormality detection unit (130) to detect abnormal signs when the water quality of the purified water, predicted based on data such as turbidity, pH, and residual chlorine concentration, exceeds a preset water quality standard.

[0065] Additionally, the artificial intelligence model of the anomaly detection unit (130) is not limited in type, but in one embodiment, the artificial intelligence model may be a first deep learning model for detecting anomalies in the raw water treatment process of the water purification facility (10).

[0066] And the raw water treatment process in which the first deep learning model configured in the anomaly detection unit (130) detects anomalies refers to the treatment process of the raw water treatment process, which is a process facility (12) constituting the water purification facility (10).

[0067] Additionally, the algorithm of the first deep learning model configured in the anomaly detection unit (130) is not limited in type, but in one embodiment, the algorithm of the first deep learning model may be a deep learning algorithm, and the deep learning algorithm used by the first deep learning model of the anomaly detection unit (130) to detect anomalies in the raw water treatment process of the water purification facility (10) may be at least one of ECOD, COPOD, and Autoencoder.

[0068] Since the deep learning algorithms described above in this invention, such as ECOD, COPOD, and Autoencoder, are commonly disclosed algorithms, a detailed explanation thereof will be omitted for convenience.

[0069] In one embodiment, the anomaly detection unit (130) may detect an anomaly in the purified water flowing into the water treatment facility (10) based on external conditions at the time when raw water flows into the water treatment facility (10) when the water quality of the purified water predicted by the water quality prediction unit (120) exceeds a preset water quality standard.

[0070] That is, the anomaly detection unit (130) can detect signs of an anomaly in the raw water treatment process of the water treatment facility (10) by using external conditions when the water quality of the purified water predicted by the water quality prediction unit (120) exceeds a preset water quality standard based on the algorithm of the first deep learning model.

[0071] In addition, the anomaly detection unit (130) preferably receives external conditions from a measuring means of the water purification plant operation management system (100) in order to detect abnormal signs of raw water flowing into the water purification facility (10).

[0072] In one embodiment, the optimal factor derivation unit (140) can derive raw water treatment process operating factors that occur when controlling the operating state of the water purification facility (10) when an abnormal sign of the raw water treatment process is detected by the abnormality detection unit (130) or when a change in the current operating state of the raw water treatment process and the characteristics of the raw water occurs, based on the algorithm of an artificial intelligence model.

[0073] At this time, the artificial intelligence model of the optimal factor derivation unit (140) is not limited in type, but in one embodiment, the artificial intelligence model may be a second deep learning model for deriving raw water treatment process operation factors.

[0074] Additionally, the raw water treatment process operating parameters are not limited, but in one embodiment, at least one of the set values ​​of the raw water treatment process may be a set value such as the power consumption, chemical input amount, agitator rotation speed, filtration time, backwashing frequency, and water quantity of the raw water treatment process, so that the water quality of the purified water to be discharged from the water purification facility (10) complies with the preset water quality standards that can be supplied to consumers.

[0075] And the algorithm of the second deep learning model configured in the optimal factor derivation unit (140) is not limited in type, but in one embodiment, the algorithm of the second deep learning model may be a deep learning algorithm, and the deep learning algorithm used by the second deep learning model of the optimal factor derivation unit (140) to derive raw water treatment process operation factors may be at least one of ECOD, COPOD, and Autoencoder, just like the anomaly detection unit (130).

[0076] Meanwhile, the water quality prediction unit (120), abnormal sign detection unit (130), and optimal factor derivation unit (140) of the present invention need to be trained, evaluated, and verified through the AI ​​solution (S10) of FIG. 2 so that they can operate according to their respective purposes based on the algorithm of the artificial intelligence model to predict water quality, detect abnormal signs, and derive operating factors of the raw water treatment process.

[0077] FIG. 2 is a flowchart illustrating the process of an AI solution for learning, evaluating, and verifying an artificial intelligence model configured in a water quality prediction unit, an anomaly detection unit, and an optimal factor derivation unit according to one embodiment of the present invention.

[0078] Referring to FIG. 2, the AI ​​solution (S10) of the present invention may proceed in the order of a data exploration and cleaning step (S11), a learning, evaluation, and verification data generation step (S12), a learning and parameter tuning step (S13), and an evaluation and verification step (S14).

[0079] In order to proceed with the above data search and refinement step (S11), the water purification plant operation management system (100) of the present invention may additionally be configured with a data search unit, a data refinement unit, and a parameter tuning unit, although these are not shown in the drawings.

[0080] In the above data search and refinement step (S11), the data search unit (not shown) receives data stored in the data collection / storage unit (110) from the data collection / storage unit (110) for the learning, evaluation, and verification of the artificial intelligence model, and can search for noise such as outliers and missing values ​​that are unnecessary for the learning of the artificial intelligence model in the received data.

[0081] In the above data exploration and refinement step (S11), the data refinement unit (not shown) can refine (preprocess) the data by removing noise discovered from the data so that the data in which noise has been discovered can be used for training an artificial intelligence model.

[0082] In the above learning, evaluation, and verification data generation step (S12), when the data refinement unit (not shown) removes noise from the data, the data can be generated as data used for learning, evaluation, and verification in the artificial intelligence models of the water quality prediction unit (120), the abnormal sign detection unit (130), and the optimal factor derivation unit (140).

[0083] In the above learning and parameter tuning step (S13), the parameter tuning unit (not shown) can select optimal parameters for the regression analysis model and the first and second deep learning models, which are artificial intelligence models, and apply a Boosting method to gradually train the regression analysis model and the first and second deep learning models.

[0084] In the above learning and parameter tuning step (S13), the parameter tuning unit can improve the performance of the regression analysis model and the first and second deep learning models based on multi-faceted tuning methods such as GridsearchCV, OPtuna, and Flaml.

[0085] Here, since the tuning methods GridsearchCV, OPtuna, and Flaml are commonly disclosed tuning methods, a detailed explanation of them will be omitted for convenience.

[0086] In the above evaluation and verification step (S14), the regression analysis model and the first and second deep learning models can be evaluated and verified to determine whether they operate to enable water quality prediction and detection of abnormal signs based on data refined in the data refinement unit (not shown) after performance improvement, and after this process, they can be configured (or mounted) in the water quality prediction unit (120), the abnormal sign detection unit (130), and the optimal factor derivation unit (140) to predict water quality, detect abnormal signs, and derive operating factors for the raw water treatment process.

[0087] Referring again to FIG. 1, the comprehensive analysis unit (150) can perform a simulation of Siranio assuming that raw water treatment process operating factors are applied to the raw water treatment process where abnormal signs are detected.

[0088] In addition, the comprehensive analysis unit (150) can derive a water quality prediction result that predicts the water quality of the raw water treatment process based on the simulation, and transmit the water quality prediction result derived based on the simulation to the digital twin system unit (180).

[0089] And the comprehensive analysis unit (150) can, after conducting a simulation, suggest an optimal operation plan for a process facility (12) to treat the water quality of purified water to a level lower than a preset water quality based on the results of the simulation, and can transmit the optimal operation plan to the digital twin system unit (180).

[0090] In one embodiment, the monitoring unit (160) monitors the raw water treatment process operating factors and operating status of the water purification facility (10) stored in real time in the data collection / storage unit (110), and can transmit the monitored raw water treatment process operating factors and operating status to the digital twin system unit (180).

[0091] In one embodiment, the facility information unit (170) can transmit facility information to the digital twin system unit (180).

[0092] At this time, the facility information may be data including the specifications, drawings, and photos of the facility (11) constituting the water purification facility (10) as shown in FIG. 3, the process facility (12) which is a subsystem of the facility (11), the equipment (13) which is a subsystem of the process facility (12), and the auxiliary equipment (14), as well as the maintenance history and CCTV surveillance screen of the facility (11), process facility (12), equipment (13), and auxiliary equipment (14).

[0093] In one embodiment, the digital twin system unit (180) can build a digital twin model to perform real-time monitoring, artificial intelligence water quality prediction, process diagnosis, and management of the water purification facility (10).

[0094] At this time, the digital twin model constructed by the digital twin system unit (180) refers to a digital twin 3D model that reproduces the water purification facility (10) as a virtual 3D graphic by applying 3D assets, and the 3D assets of the water purification facility (10) can be structured as shown in FIG. 3.

[0095] FIG. 3 is a drawing for explaining the 3D asset structuring of a water purification facility according to one embodiment of the present invention.

[0096] Referring to FIG. 3, the water purification facility (10) in the present invention may consist of a facility (11), a process facility (12) which is a subsystem of the facility (11), an equipment (13) which is a subsystem of the process facility (12), and an auxiliary equipment (14).

[0097] In one embodiment, the facility (11) refers to a building of the water purification facility (10), such as an administration building, a pharmaceutical building, etc.

[0098] In one embodiment, the process facility (12) refers to a raw water treatment process for treating raw water flowing into the water purification facility (10) into purified water that satisfies a preset water quality, and the raw water treatment process of the water purification facility (10) can be configured as shown in FIG. 4.

[0099] FIG. 4 is a block diagram illustrating an example of a process facility according to one embodiment of the present invention.

[0100] Referring to FIG. 4, the process facility (12) may consist of a mixing tank, a coagulation sedimentation tank (12a), a sedimentation / filtration tank (12b), a water purification tank (12c), etc.

[0101] At this time, the coagulation sedimentation tank (12a) administers a coagulant to the raw water flowing into the water purification facility (10) to cause large suspended solids in the raw water to coagulate into the form of lumps (flocs), and then allows the flocs to settle by gravity, thereby reducing the turbidity of the raw water and removing suspended solids.

[0102] Additionally, the sedimentation / filtration tank (12b) allows flocs that were not removed in the coagulation sedimentation tank (12a) to settle, and then removes fine particles, suspended solids, and some organic substances from the raw water through a filtration layer such as sand, gravel, and activated carbon, thereby allowing the raw water to be converted into purified water.

[0103] And the water purification tank (12c) may be treated with a disinfectant such as chlorine or ozone, or additional treatment may be performed if necessary, to store the water while maintaining its quality after removing pathogens and microorganisms from the purified water filtered in the sedimentation / filtration tank (12b).

[0104] Raw water flowing into the water treatment facility (10) can be treated step-by-step as it passes through the mixing tank - coagulation sedimentation tank (12a) - sedimentation / filtration tank (12b) - water purification tank (12c) of the process facility (12) and then discharged as purified water.

[0105] Referring again to FIG. 3, the equipment (13) refers to a pump, valve (13a) and motor (13b), etc. of a process facility (12) capable of operation (or operation).

[0106] The auxiliary equipment (14) refers to the platform and fence of the process facility (12) that is not configured to operate (or be operated), unlike the equipment (13) that can be operated.

[0107] FIG. 5 is a block diagram illustrating an example of a 3D asset model of a water purification facility according to one embodiment of the present invention.

[0108] Referring to FIG. 5, the digital twin system unit (180) can apply 3D assets of the facility (11), process facility (12), equipment (13), and auxiliary equipment (14) of the water purification facility (10) to extract a facility 3D asset model (20a), a process facility 3D asset model (20b), an equipment 3D asset model (20c), and an auxiliary equipment 3D asset model (20d).

[0109] Additionally, the digital twin system unit (180) can systematize the relationships between 3D asset models by configuring the asset hierarchy so that the facility 3D asset model (20a) is a higher system than the process facility asset model (20b), and the process facility asset model (20b) is a higher system than the equipment 3D asset model (20c) and auxiliary equipment 3D asset model (20d).

[0110] And the digital twin system unit (180) can display 3D asset models (20a~20d) in the virtual space of the digital twin 3D model based on the configuration of the asset layer.

[0111] In addition, the digital twin system unit (180) displays the water quality prediction results and optimal operation plan received from the comprehensive analysis unit (150), the raw water treatment process operation factors and operation status of the water purification facility (10) received from the monitoring unit (160), and the facility information received from the facility information unit (170) in the virtual space of the digital twin 3D model.

[0112] In addition, the digital twin system unit (180) can structure the 3D asset (20) for building the digital twin 3D model to have the attributes of a driving unit (21), which is a set of dynamic information, and an information unit (22), which is a set of static information.

[0113] At this time, the driving unit (21) can reflect the physical behavior of the water purification facility (10) in the digital twin 3D model into the virtual space of the digital twin 3D model, and the physical behavior of the water purification facility (10) can be displayed in the virtual space through the driving unit (21) of the digital twin 3D model.

[0114] These driving units (21) may be physical behaviors or a set of motions or movements for expressing dynamic movements of 3D assets (20) (e.g., minimum number of rotations, maximum number of rotations, range of motion 0 to 180°, etc.) in virtual space.

[0115] Additionally, the information unit (22) can reflect static items (e.g., width, height, weight, etc.) of the water purification facility (10) of the 3D asset (20) in the virtual space of the digital twin 3D model, and the digital twin 3D model can implement the water purification facility (10) in the virtual space through the information unit (22).

[0116] Meanwhile, the 3D asset (20) of the present invention has the attribute of an information part (22) regardless of whether physical movement occurs, and in the case of an asset model that requires driving, such as a valve or motor, it may additionally have the attribute of a driving part (21).

[0117] In one embodiment, the 3D asset (20) for displaying the physical behavior of the water purification facility (10) in the virtual space of the digital twin 3D model may be a process facility 3D asset model (20b) or an equipment 3D asset model (20c) where the physical behavior occurs.

[0118] Below, I will explain in detail the physical behavior of the water purification facility (10) based on the facility 3D asset model (20c).

[0119] FIG. 6 is a drawing illustrating an example of a 3D asset model of a valve, which is a facility according to one embodiment of the present invention.

[0120] Referring to FIG. 6(a), conventionally, it was possible to display only the opening rate information of the valve (13a') in the virtual space of the digital twin model as text in % units using a 3D asset model of the valve (13a').

[0121] Referring to FIG. 6(b), the 3D asset model (20c) of the valve (13a) corresponding to the equipment (13) is configured such that, unlike conventional methods, the opening state of the valve (13a) is displayed together with the valve (13a) in the virtual space of the digital twin 3D model.

[0122] In addition, the 3D asset model (20c) of the valve (13a) not only provides information on the opening rate of the valve (13a), but also, unlike conventional methods, allows the change in the amount of returned sludge according to the opening rate of the valve (13a) to be displayed as an animation effect in the virtual space of the digital twin 3D model.

[0123] FIG. 7 is a drawing illustrating an example of a 3D asset model of a motor, which is a piece of equipment according to one embodiment of the present invention.

[0124] Referring to FIG. 7(a), conventionally, the 3D asset model of the motor (13b') is applied in a form where the frame surrounds the rotor where physical movement occurs, so the physical movement is not displayed in the virtual space of the digital twin model.

[0125] Referring to FIG. 7(b), the 3D asset model (20c) of the motor (13b) corresponding to the equipment (13) is configured such that, unlike conventional methods, the motor (13b) is displayed in the virtual space of the digital twin 3D model in a manner that distinguishes between the components where physical movement occurs and the remaining components where physical movement does not occur, so that the rotor where physical movement occurs is visualized.

[0126] At this time, the information for the driving unit (21) that causes the information unit (22) to be displayed in the virtual space of the digital twin 3D model refers to at least one of the information for the facility (11), process facility (12), equipment (13) and auxiliary equipment (14) configured in the water purification facility (10) where actual physical movement occurs.

[0127] As a specific example, the information section (22) includes items such as the size, rotational speed, and power consumption of the pump when assuming that the driving section (21) where actual physical movement occurs is the pump of the equipment (13).

[0128] FIG. 8 is a diagram illustrating a method of providing a digital twin 3D model according to an embodiment of the present invention.

[0129] Referring to FIG. 8, the digital twin system unit (180) can present a digital twin 3D model to the user in the form of a user interface.

[0130] Meanwhile, the water purification plant operation management system (100) may further be equipped with a visualization unit (190) for providing a digital twin 3D model built through the digital twin system unit (180) to the user.

[0131] The visualization unit (190) may be a VR system capable of executing content based on a VR platform that can be mounted on the user's body, and the hardware of the VR system may be a VR headset (HMD), controller, tracker, sensor, computer or console, etc.

[0132] That is, the virtual space of the digital twin 3D model of the present invention can be provided to the user in a VR environment when the user attaches the visualization unit (190) to their body.

[0133] FIG. 9 is a schematic diagram illustrating the configuration of a digital twin system unit according to one embodiment of the present invention.

[0134] Referring to FIG. 9, the digital twin system unit (180) includes an image unit (181), a function unit (182), an information unit (183), and a linkage unit (184).

[0135] In one embodiment, the image unit (181) combines 3D assets (20) from a structured digital 3D model asset library DB to configure the main screen modeling of the virtual space of the digital twin 3D model, and the world view mode can be configured by default.

[0136] At this time, the world view mode may be a video mode that extensively displays the virtual space of the digital twin 3D model.

[0137] In addition, the video unit (181) can build an FPS mode using world view modeling and add a VR mode for virtual experience.

[0138] And the video unit (181) is linked with the visualization unit (190) to enable VR mode, and 3D video can be provided through Real Mode and Fantastic Mode.

[0139] Here, Real Mode is a video mode that emphasizes realistic expression and reproduces the actual environment as realistically as possible, and is used in video for operation, while Fantastic Mode is a video mode that emphasizes unrealistic or dramatic elements unlike Real Mode and can be used for external promotion of the water purification plant operation management system (10).

[0140] In one embodiment, the function unit (182) may be composed of a function that controls process navigation and movement modes in the virtual space of the digital twin 3D model, a basic information window that displays facility specifications and monitoring data, a HUD information window that displays measurement information in real time, and an object information window that displays related information such as process diagnosis, asset management, and maintenance history.

[0141] In one embodiment, the information unit (183) is a function responsible for managing internally stored information and externally linked data, and can perform the role of internally classifying and analyzing the information of the linkage unit (184) and transmitting it to the function unit (182).

[0142] In one embodiment, the linkage unit (184) is configured together with the driving unit (21) and the information unit (22) to implement a digital twin 3D model, and can perform the role of an interface that collects necessary data from external systems such as HMI, process diagnostic system, and asset management system, which are external linkage targets, and provides internal information to the outside.

[0143] In this case, information provision is managed using the standard RestAPI method, and information collection can be designed to be possible in various ways, such as DB to DB, RestAPI, and file to DB.

[0144] In addition, the linkage unit (184) complies with the National Intelligence Service's security guidelines and personal information protection regulations when linking the system, and may include security that strengthens security through a one-way linkage device in accordance with the policy of separate operation of the administrative network and the control network.

[0145] As described above, the detailed description of the preferred embodiments of the present invention disclosed is provided to enable those skilled in the art to implement and practice the present invention. Although the present invention has been described with reference to preferred embodiments, those skilled in the art will understand that various modifications and changes can be made to the present invention without departing from the scope of the invention. For example, those skilled in the art may utilize each configuration described in the embodiments described above in combination with one another. Accordingly, the present invention is not intended to be limited to the embodiments shown herein, but to be given the broadest scope consistent with the principles and novel features disclosed herein.

[0146] The present invention may be embodied in other specific forms without departing from the technical spirit and essential features of the invention. Accordingly, the above detailed description should not be interpreted restrictively in all respects but should be considered exemplary. The scope of the invention shall be determined by a reasonable interpretation of the appended claims, and all modifications within the equivalent scope of the invention are included within the scope of the invention. The invention is not intended to be limited to the embodiments shown herein, but to be given the broadest possible scope consistent with the principles and novel features disclosed herein. Furthermore, embodiments may be constructed by combining claims that are not explicitly related in the claims, or included as new claims through amendments made after filing.

[0147] The digital twin-based water purification plant operation management system of the present invention enables real-time monitoring, artificial intelligence water quality prediction, process diagnosis, and management of water purification facilities by constructing a digital twin 3D model, thereby enabling proactive maintenance of water purification facilities. At the same time, it can provide a digital twin 3D model in which 3D assets are structured, consisting of a driving unit for reflecting the physical behavior of water purification facilities in the virtual space of the digital twin 3D model and an information unit composed of information about the driving unit, thus having industrial applicability.

Claims

1. A data / collection storage unit that collects and stores data including operating parameters and operating status of the raw water treatment process of a water purification facility in real time; A water quality prediction unit that predicts the water quality of purified water according to the raw water treatment process of the water treatment facility using operating parameters of the raw water treatment process of the water treatment facility based on the algorithm of an artificial intelligence model; An anomaly detection unit that detects abnormal signs in the raw water treatment process of the water purification facility when the water quality of the purified water predicted by the water quality prediction unit exceeds a preset water quality standard based on the algorithm of the artificial intelligence model; An optimal factor derivation unit that derives raw water treatment process operating factors generated when controlling the operating state of the water purification facility based on an artificial intelligence model algorithm, when an abnormal sign of the raw water treatment process is detected by the anomaly detection unit, or when a change in the current operating state of the raw water treatment process and the characteristics of the raw water occurs; and A digital twin-based water purification plant operation management system characterized by comprising: a digital twin system unit comprising: a digital twin 3D model that reproduces the water purification facility as a virtual 3D graphic by applying the 3D asset of the water purification facility; a driving unit that is a set of dynamic information for reflecting the physical behavior of the water purification facility in the virtual space of the digital twin 3D model, and an information unit that is a set of static information, wherein the 3D asset is structured to have the attributes of a driving unit and an information unit, respectively; and a linkage unit that performs the role of an interface for data collection and information provision together with the driving unit and the information unit.

2. In Paragraph 1, A digital twin-based water purification plant operation management system characterized by further including a comprehensive analysis unit that conducts a simulation of a scenario assuming that the above-mentioned raw water treatment process operation factors are applied to the above-mentioned raw water treatment process, and derives a water quality prediction result that predicts the water quality of the above-mentioned raw water treatment process based on the simulation.

3. In Paragraph 2, The above raw water treatment process operating parameters are, A digital twin-based water purification plant operation management system characterized in that the water quality of the above-mentioned purified water is a set value of at least one of the raw water treatment process, such as power consumption, chemical input amount, agitator rotation speed, filtration time, backwashing frequency, and water quantity, in order to comply with the above-mentioned preset water quality standards.

4. In Paragraph 2, The above digital twin system unit is, A digital twin-based water purification plant operation management system characterized by displaying the above water quality prediction results in the virtual space of the above digital twin 3D model.

5. In Paragraph 1, A digital twin-based water purification plant operation management system characterized by further including a monitoring unit that monitors the raw water treatment process operation factors and operation status of the water purification facility stored in real time in the data collection / storage unit.

6. In Paragraph 5, The operating status of the above water purification facility is, A digital twin-based water purification plant operation management system characterized by including the operational status of a process facility, which is a subsystem of the facility constituting the above-mentioned water purification facility, and an equipment, which is a subsystem of the above-mentioned process facility.

7. In Paragraph 5, The above digital twin system unit is, A digital twin-based water purification plant operation management system characterized by displaying the raw water treatment process operation parameters and operation status of the above-mentioned water purification facility in the virtual space of the above-mentioned digital twin 3D model.

8. In Paragraph 1, A digital twin-based water purification plant operation management system characterized by further including a facility information unit that transmits the facility information to the digital twin system unit so that the facility information is displayed in the virtual space of the digital twin 3D model.

9. In Paragraph 8, The above facility information is, A digital twin-based water purification plant operation management system characterized by data including the specifications, drawings, and photographs of the facilities constituting the water purification facility, the process facilities which are subsystems of the facilities, and the equipment and auxiliary equipment which are subsystems of the process facilities, as well as the maintenance history and CCTV surveillance screens of the facilities, process facilities, equipment and auxiliary equipment.

10. In Paragraph 9, The above digital twin system unit is, A digital twin-based water purification plant operation management system characterized by displaying the above facility information in the virtual space of the above digital twin 3D model.

11. In Paragraph 9, The above digital twin system unit is, After applying 3D assets of the above-mentioned water purification facility, process facility, equipment, and auxiliary equipment to extract 3D asset models of the said facility, process facility, equipment, and auxiliary equipment respectively, an asset hierarchy is configured such that the facility 3D asset model is a higher-level system to the process facility 3D asset model, and the process facility 3D asset model is a higher-level system to the equipment 3D asset model and auxiliary equipment 3D asset model, thereby systematizing the relationships between the 3D asset models. A digital twin-based water purification plant operation management system characterized by displaying 3D asset models of facilities, process facilities, equipment, and auxiliary equipment in the virtual space of the digital twin 3D model based on the configuration of the asset hierarchy.

12. In Paragraph 11, The above process facility is, A digital twin-based water purification plant operation management system characterized by a raw water treatment process including a mixing tank, a coagulation sedimentation tank, a sedimentation / filtration tank, and a purified water tank for carrying out the above raw water treatment process.

13. In Paragraph 11, The above equipment is, A digital twin-based water purification plant operation management system characterized by pumps, valves, and motors operated to carry out the above raw water treatment process.

14. In Paragraph 11, The above auxiliary equipment is, A digital twin-based water purification plant operation management system characterized by a platform and a fence installed on the above-mentioned facility.

15. In Paragraph 1, The above digital twin system unit is, A digital twin-based water purification plant operation management system characterized by presenting the above-mentioned digital twin 3D model to a user in the form of a user interface.

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