KR101963124B1 - Remote diagnosing and managing system for small-scaled desalination equipment being separately installed in island area, and method for the same - Google Patents

Abstract

Provided is a system for diagnosing and managing a remote small-scaled desalination apparatus separately installed in an island area, and a method thereof. According to the present invention, the system determines an optimal operation variable in a site or a remote plate in consideration of characteristics and amount of water at a place, where a small-scaled desalination apparatus is installed, to minimize operation variance in a process. Moreover, the system monitors a water permeation coefficient and a relative permeability index for process diagnosis to provide an operator with an alarm function for contamination of a separation membrane and detects a replacement time of the separation membrane in advance based on an artificial neural network model to stably and safely supply water without interrupting the water in an island area. Moreover, the system calculates an operation flux and a recovery rate from data, such as simplified process information (type and connection configuration of the separation membrane), a flow rate, total dissolved solids (TDS), water temperature, and the like, acquired by a water quality sensor by using dissolved scattering material transfer and concentration polarization models, and transfers an operation result to the site in the remote plate or reflects the same in decision making of the operator in the site.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a remote diagnosis management system and a remote diagnosis management system of a small scale desalination apparatus installed in a book,

The present invention relates to a remote diagnosis management system for a small scale desalination apparatus, and more particularly, to a remote diagnosis management system for a small scale seawater or an underwater desalination apparatus (hereinafter referred to as a small scale desalination apparatus) The present invention relates to a remote diagnosis management system and a method for a small-scale desalination apparatus for determining an optimal flux and recovery rate based on an artificial neural network model and predicting a membrane replacement timing based on an artificial neural network model.

 Generally, according to the salt concentration aspect, Brackish Water refers to the middle of sea water and fresh water. In addition, nadir and fresh water are generally classified according to a salt concentration of 500 mg / L, but the nadir is classified as a salt concentration of not less than 1,000 mg / L and not more than 5,000 mg / L in water. In the case of seawater, it is known that the salt concentration in water in each region is in the range of several thousand mg / L to less than 45,000 mg / L, theoretically having a salt concentration of 35,000 mg / L.

Distillation method, electrodialysis method, reverse osmosis separation method, membrane distillation technique, etc. are applied for desalination of seawater or nautical water of domestic and overseas books. However, due to the nature of the book, consideration is given to limit of electricity supply, limit of available site and limit of installation cost / operation cost. Recently, the technology using the membrane has been applied most.

For example, the desalination process of seawater or nautical water using a separation membrane is generally carried out by the following processes: Intake → Pre-treatment process → Reverse Osmosis membrane process → Post-treatment process → Storage And the like.

On the other hand, when we look at domestic cases, out of the total 3,167 libraries, 500 inhabitants are inundated, and about 250 books are experiencing frequent irrigation droughts. These 250 sites depend on simple waterworks, wells, transport water supply, etc. In particular, about 90 booklets are supplied with daily water through small-scale seawater or sea water desalination equipment. Considering the above-mentioned small-scale seawater or sea water desalination apparatus exported from abroad as well as the domestic book site, the number is further increased. Therefore, in order to secure competitiveness with other facilities, it is necessary to implement the system in a short time with the standardized process configuration, so that it is possible to have excellent economical efficiency. Moreover, the mobility must be ensured so that the process assembly and repair are easy, The replacement period of the filter and the separator must be precisely predicted beforehand so that the operation convenience can be obtained.

Specifically, in the case of domestic book sites, small-scale seawater or nautical desalination plants of 70% or more are installed in the South Sea and the West Sea. Even though the same design and construction are carried out in the same site, the operating conditions of the reverse osmosis membrane, which is a core facility, are different from each other. Therefore, the recovery rate of the desalination equipment, the treated salt concentration and the power consumption are varied.

Accordingly, an accurate solution to the self-diagnosis of the desalination device and the replacement cycle of the core equipment should be provided first in order to reduce the operating and maintenance deviation of a large number of dispersed small-scale seawater or desalination plant.

The purpose of this solution is to ultimately identify the current process operation status in real time by using scientific analysis tools and programs including the driver, manager, supervisor and so on, The quality of the fresh water produced through real-time process optimization should be maintained at the highest level, such as an attempt to predict the treatment state, to solve problems related to the operation of the process, or to improve it.

Through the standardized data construction method and the program that implements algorithm that enables process diagnosis and life prediction through model analysis with simplified pressure and flow sensor, and file transfer and merging process of the minimized capacity size of remote site, By monitoring the process operation status of the distributed water treatment system, it is possible to reduce the variation in operation and maintenance of a large number of dispersed small-scale seawater or water desalination apparatus.

In addition, it is possible to secure the reliability between producers and consumers by performing maintenance by preemptive countermeasures for small scale desalination devices distributed in the book site, and by understanding the replacement cycle of many key facilities in advance, Technology can minimize maintenance costs.

As a result, the problems of the small-scale seawater or the water desalination apparatus installed and operated in the domestic and overseas bookstores according to the related art can be roughly classified into three.

First, there is a problem that the recovery rate after the construction is kept low due to the failure of the process design that does not take into account the volatility of long-term water quality data at the design stage. Second, as the recovery rate is kept low, it is linked to the increase of the power consumption, which causes the maintenance cost to increase. At the same time, there is a problem that the operation variable varies depending on the place of installation, operation time and season. Third, since there is no preemptive operation and maintenance system for several tens to several thousands of small desalination plants, there is a high possibility of operation problems due to singularity. Therefore, these causes increase the operation cost of the small-scale seawater or the desiccant desalination apparatus, and this causes a problem of the burden on the consumer.

On the other hand, as a prior art, Korean Patent Registration No. 10-1706452 discloses an invention called " method for real-time detection of a membrane anomaly state " In summary,

First, in order to detect the abnormal state of the membrane, the osmotic pressure relation considering the temperature correction is theoretically calculated by comparing the measured value with the state without the abnormal state, and the corrected product quantity is calculated through the least squares method Use the model formula to determine. Second, the temperature, flux, and recovery rate are varied in order to predict the reverse osmosis membrane cleaning time, and the temperature, pressure, flow rate, and water quality experiment data are collected and stored in the operation data storage section. Third, the set of continuous performance data groups including the total performance data group from the base point to the real-time detection point and the real-time detection point including the real-time detection point are set as the respective sample groups, and the abnormality is determined through the t-test.

However, the model equation proposed in the real-time detection method of the membrane anomaly state according to the prior art is based on the basic equation about the mass transfer of the reverse osmosis membrane, but the data anomaly due to membrane contamination is a nonlinear model of the high- There is an error that the uncertainty coefficient is determined by law, and the reliability verification algorithm for the use of the decision model of this model is excluded. In other words, nonlinear models for membrane contamination are due to various causes such as particulate matter, organic matter, microorganisms, and scale-inducing substances.

Further, in the case of the real-time detection method of a separation membrane abnormality state according to the related art, since the theoretical calculated value of the steady state model and the actual measurement value always deviate, and the precondition of the model is a steady- It is difficult to distinguish whether it is the judgment of abnormal data or the data due to membrane contamination.

Also, in the case of the real-time detection method of a separation membrane abnormality state according to the prior art, it is necessary to consider a temperature deviation of at least 6 months even in consideration of seasonal fluctuation in repeatedly testing water temperature, flux and recovery rate in order to determine a cleaning time Furthermore, there is a problem that the pressure or the valve needs to be adjusted rather than the flux because the target process in the field is a constant flow rate operation.

As another prior art, Korean Patent No. 10-1533554 discloses an invention entitled " Apparatus and Method for Monitoring Real Time Film Fouling in Reverse Osmosis Membrane ".

A real-time film contamination monitoring apparatus in a reverse osmosis membrane vessel according to the related art includes an RO processing simulation unit for classifying a reverse osmosis membrane vessel into RO and RO by performing RO treatment, A reverse osmosis membrane vessel, and a separate temperature control unit in the storage unit.

However, in the case of a real-time film contamination monitoring apparatus in a reverse osmosis membrane vessel according to the related art, there is a problem that it is expensive to use for a large-scale plant, and even if it is implemented as a small system in a production site, Which is a problem in that it must be simulated by repeated long-term experiments.

As another prior art, Korean Patent Laid-Open Publication No. 2013-85220 discloses an invention named " a seawater desalination device having a membrane contamination diagnosis function of reverse osmosis membrane ".

A seawater desalination apparatus having a function of diagnosing a membrane contamination of a reverse osmosis membrane according to the prior art is provided with information on inflow water flowing into the seawater treatment apparatus from at least one sensor installed in the seawater treatment apparatus and information on the flow rate and pressure of water conveyed in the seawater treatment apparatus A transmission / reception unit for receiving the information about the mobile station; And a control unit for calculating the amount of the substance contained in the reference unit water based on the flow rate and the pressure to calculate the degree of contamination of the reverse osmosis membrane module in the seawater treatment apparatus and performing a diagnosis and a trouble coping function according to the calculated degree of contamination do.

The seawater desalination apparatus having the function of diagnosing the membrane contamination of the reverse osmosis membrane according to the prior art enables an early diagnosis of membrane contamination. As a result, it is advantageous in real-time measurement and information of the sensor such as a flow meter, a pressure gauge, There is a limit to diagnose membrane contamination indirectly through analysis.

Korean Patent No. 10-1706452 filed on Apr. 30, 2015, entitled " Method for real-time detection of membrane anomaly state " Korean Patent No. 10-1533554 filed on Mar. 5, 2015, entitled " Apparatus and Method for Monitoring Real Time Film Fouling in Reverse Osmosis Membrane " Korean Patent No. 10-1655965 filed on April 19, 2016, entitled " Small Seawater Desalination Facility for Island Water Treatment in a Book Area " Korean Patent Laid-Open Publication No. 2013-85220 (published on July 29, 2013), entitled " Seawater desalination apparatus having a membrane pollution diagnosis function of reverse osmosis membrane " Korean Patent No. 10-546851 filed on November 14, 2005, entitled " Water and water desalination water purification automation system for drinking water supply in areas where water supply is not provided " Korean Patent No. 10-1624911 filed on Dec. 18, 2013, entitled " High-Efficiency Seawater Desalination Operation in Island Areas Using Renewable Energy, o Linkage System and Seawater Desalination & Korean Patent Publication No. 2016-59038 (Publication date: May 26, 2016), entitled " Real-time Water Quality Measurement and Water Purification System Using Network " Korean Patent Publication No. 2017-51988 (Publication Date: May 12, 2017), entitled " Membrane Fouling Monitoring System of Membrane System " Japanese Patent No. 4,899,875 filed on January 12, 2007, entitled " Water Treatment Device " Japanese Patent No. 5,079,372 filed on April 9, 2007, entitled " Membrane Separation Method and Membrane Separation Device "

Technical Solution According to an aspect of the present invention, there is provided a method for remotely diagnosing and managing a small-scale seawater or an aquatic desalination apparatus, the method comprising: determining optimal flux and recovery rate based on a model predictive controller; The present invention is to provide a remote diagnosis management system and a method for a small-scale desalination apparatus installed in a book on the basis of model-based prediction of a membrane replacement time.

Another object of the present invention is to provide a warning function for separator membrane pollution degree by monitoring the water permeability coefficient and relative permeability index for process diagnosis and detecting the membrane replacement time in advance by the artificial neural network model A remote diagnosis management system and a method for a small scale desalination apparatus installed in a book, which can supply water steadily and safely to enable the unauthorized use of the book site.

Another object of the present invention is to provide a method and system for analyzing data obtained by a water quality sensor such as simplified process information (separation membrane type and connection composition), flow rate and total dissolved solids (TDS), water temperature, Provided is a remote diagnosis management system and method for a small-scale desalination apparatus installed in a book site, which can calculate operation flux and recovery rate using the system, and transmit the information to a site in a remote place or reflect it in a driver's decision in the field .

As a means for achieving the above technical object, a remote diagnosis management system of a small-sized desalination apparatus installed in a book paper according to the present invention is distributed on a book, and the seawater or nose is desalinated by a reverse osmosis Small scale desalination equipment; A process management and control unit for remotely collecting operational data and sensor data from the distributed small scale desalination apparatus, inputting corresponding process information, and monitoring the quality and quantity of the small scale desalination apparatus; An optimal operation variable determining unit for determining an optimal flux and a recovery rate corresponding to the remote collected operation data for optimum operation of the small scale desalination apparatus based on a model predictive controller; A process index monitoring unit for monitoring a water permeability coefficient and a relative permeability index to provide a warning function for the membrane contamination degree for the process diagnosis of the small scale desalination unit; And a membrane replacement timing predicting unit for predicting a replacement timing of the reverse osmosis module membrane corresponding to the membrane contamination degree using an artificial neural network model.

Here, the process management and control unit may include: a process information input unit for inputting a process configuration according to a separation membrane type and separation membrane connection conditions as process information according to operation data collected for the small-scale desalination apparatus; And a water quality and quantity monitoring unit for measuring the influent water temperature collected from the influent water sensor of the small scale desalination apparatus, the concentration (TDS) of total dissolved solids, the concentration and quantity of boron,

Here, the optimum operating parameter determiner determines the operation flux and the recovery rate based on the optimization model predictive controller based on the solution diffusion model of the reverse osmosis membrane of the small-scale desalination apparatus and the process simulation according to the operating variables. Can be performed.

Here, the solution diffusion material transfer model of the optimum operation variable determining unit considers the mass transfer coefficients of the reverse osmosis membrane types, takes into account the temperature correction of the influent water, the osmotic pressure and the pressure loss per membrane, and includes the concentration polarization index .

Here, the optimization model predictive controller of the optimum operating parameter determiner may limit the concentration of the total dissolved solids, the treated water quality for the harmful substances of boron, and the power consumption consumed in the small-scale desalination apparatus, After determining the recovery rate and the optimum range of the flux according to these constraints, the optimum flux and recovery rate are determined, and finally the pressure of the high pressure pump, which is an operating variable, can be determined.

Here, the process index monitoring unit continuously calculates the water permeability coefficient and calculates the relative water permeability index. If the process capability index of the initial water permeability is continuously decreased by 30% or more from the initial permeability, It is preferable to generate a warning that an abnormality has occurred.

Here, if the value calculated by the artificial neural network model reaches a user's reference value after a warning that an abnormality has occurred in the process production capacity from the process index monitoring unit, the separation membrane replacement time predicting unit predicts It is possible to provide the manager of the small-scale desalination apparatus with information on the membrane replacement time.

Here, the separation membrane replacement time predicting unit can remotely diagnose and manage a plurality of distributed small-scale desalination apparatuses by making a decision to determine the degree of contamination of the separation membrane and the replacement time by the artificial neural network model without stopping the operation of the small- .

Herein, the input factors of the artificial neural network model of the membrane replacement time predicting unit are water temperature, total dissolved solids (TDS), pressure difference (TMP) on the inflow side and concentrated water side of the membrane, flow rate and operation time, .

Here, the optimum operating parameter determiner and the separator replacement timing predictor are implemented in a field controller of a book magazine to determine operating conditions of the optimum flux and high-pressure pump by themselves, and an alarm notifying the replacement time of the membrane, Can be diagnosed.

In another aspect of the present invention, there is provided a remote diagnosis management method for a small-scale water treatment apparatus installed on a book paper, comprising the steps of: a) a small-scale desalination apparatus distributed on a book, Producing fresh water by a reverse osmosis process; b) the process control and control unit collecting sensor data and operation data from each of said small-scale desalination units via a virtual private network; c) inputting the membrane type and the process configuration according to the operation data of the reverse osmosis process of the small scale desalination apparatus; d) monitoring water quality and quantity according to the sensor data collected from the influent water sensor of the small scale desalination apparatus; e) determining optimal flux and recovery rate, which are optimum operating parameters for optimum operation of the small scale desalination apparatus, based on optimum operating parameter determination addition model predictive controller; f) diagnosing and monitoring the process of the small scale desalination apparatus by calculating and comparing the water permeability coefficient and the relative permeability index corresponding to the process index monitoring unit separation membrane pollution degree; And g) estimating a separation membrane replacement time corresponding to the separation membrane contamination degree using an artificial neural network model, wherein the process management and control unit in step b) collects the small- A process information input unit for inputting a process configuration according to a separation membrane type and a separation membrane connection condition as process information according to the operation data; And a water quality and quantity monitoring unit for measuring the influent water temperature collected from the influent water sensor of the small scale desalination apparatus, the concentration (TDS) of total dissolved solids, the concentration and the quantity of boron, and databaseing them.

According to the present invention, for a small-scale desalination apparatus including a microfiltration filter, a reverse osmosis membrane, and other facilities for seawater or nursing desalination processing distributed in domestic and overseas papers, process composition and dissolution Based on the optimization model of the diffusion mass transfer model and the operating parameters (flux and recovery rate), it is possible to minimize the operating variability of the process by determining the optimum operating parameters at the site or remote site considering the water quality and quantity of the place where the small- have.

According to the present invention, the water permeability coefficient and the relative permeability index are monitored to provide a warning function to the operator on the membrane pollution degree for the process diagnosis, and the artificial neural network model is used to detect the time So that water can be supplied stably and safely.

According to the present invention, it is possible to provide a manager who is required to provide quality control for designing, construction and operation of a small-sized seawater or a sea water desalination apparatus installed in several tens to thousands of booklets, have.

According to the present invention, data obtained from a water quality sensor such as simplified process information (separation membrane type and connection composition), flow rate and total dissolved solids (TDS), water temperature, and the like are used to calculate the operation flux and recovery rate And can transmit the information to the site at a remote place or reflect it in the driver's decision in the field.

According to the embodiment of the present invention, it is possible to determine the reverse osmosis membrane contamination degree using the temperature-compensated water permeability coefficient and the relative permeability index, and to predict the membrane replacement time based on the artificial neural network model.

According to the embodiment of the present invention, it is possible to monitor the self-diagnosis and remote-phase operation state of a plurality of distributed small-scale desalination apparatuses.

1 is a schematic block diagram of a remote diagnosis management system of a small scale desalination apparatus installed on a book paper according to an embodiment of the present invention.
FIG. 2 is a view illustrating an example of collecting sensor data and operation data from the small-scale desalination apparatus shown in FIG. 1 to determine optimum operating parameters and predicting a membrane replacement time.
3 is a schematic view of a dissolution diffusion mass transfer model of the reverse osmosis membrane of the small-scale desalination apparatus shown in FIG.
4 is a diagram illustrating an algorithm for determining model predictive controller-based optimal flux and high pressure pump operating pressure for a reverse osmosis module of the small scale desalination apparatus shown in FIG. 2;
5 is a diagram illustrating a simulation of a program for determining a model predictive controller-based optimum flux and a high-pressure pump operating pressure of a small-scale desalination apparatus in a remote diagnosis management system of a small-scale desalination apparatus installed in a book paper according to an embodiment of the present invention .
Figure 6 is a diagram illustrating the results of a reverse osmosis optimization of a program that determines the model predictor controller-based optimal flux and high pressure pump operating pressure of a small scale desalination plant.
Figure 7 is a diagram showing the results of determining the model predictive controller-based optimal flux and recovery of a small scale desalination plant.
8 is a diagram for explaining prediction of a process anomaly alarm and a membrane replacement time of a small scale desalination apparatus based on an artificial neural network model in a remote diagnosis management system of a small scale desalination apparatus installed on a book paper according to an embodiment of the present invention .
FIG. 9 is a diagram illustrating the structure of an input layer, a hidden layer, and an output layer of an artificial neural network model for predicting a membrane replacement time of the small-scale desalination apparatus shown in FIG.
10 is a diagram illustrating a configuration of an artificial neural network model implemented as a program for predicting a replacement time of a membrane of a small scale desalination apparatus in a remote diagnosis management system of a small scale desalination apparatus installed in a book paper according to an embodiment of the present invention.
11 is a diagram illustrating a result of prediction of a membrane replacement time according to the artificial neural network model shown in FIG.
12 is a flowchart illustrating an operation of a remote diagnosis management method for a small scale desalination apparatus installed in a book on a book according to an embodiment of the present invention.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings, which will be readily apparent to those skilled in the art. The present invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. In order to clearly illustrate the present invention, parts not related to the description are omitted, and similar parts are denoted by like reference characters throughout the specification.

Throughout the specification, when an element is referred to as " comprising ", it means that it can include other elements as well, without excluding other elements unless specifically stated otherwise. Also, the term " part " or the like, as described in the specification, means a unit for processing at least one function or operation, and may be implemented by hardware, software, or a combination of hardware and software.

[Remote Diagnosis Management System of Small Scale Desalination Installed on the Island (100)]

FIG. 1 is a schematic diagram of a remote diagnosis management system of a small-scale desalination apparatus installed on a book paper according to an embodiment of the present invention. Referring to FIG. 1, Fig.

Referring to FIG. 1, a remote diagnosis management system 100 of a small scale desalination apparatus installed on a book paper according to an embodiment of the present invention includes a small-scale desalination apparatus 110, a process management and control unit 120, A process index monitoring unit 140, and a separation membrane replacement timing predicting unit 150.

The small-scale desalination apparatus 110 is a plurality of small-scale desalination apparatuses 110a to 110n dispersed on a book, and desalination or sea water is desalinated by a reverse osmosis (RO) system by a reverse osmosis module 116 described later.

The process management and control unit 120 remotely collects operation data and sensor data from the distributed small scale desalination apparatus 110 via a virtual private network (VPN), inputs corresponding process information, Monitor water quality and quantity of small scale desalination equipment. Here, the virtual private network (VPN) refers to a corporate communication service that can greatly reduce the cost of a line by using a public network such as an Internet network as a private network, and can only be connected to a communication network of an enterprise and an Internet service provider. It does not require expensive equipment or software to be purchased and managed. It can save 20 ~ 80% more than traditional leased line, and it has user's mobility guarantee and convenient network configuration. Although the remote diagnosis management system 100 of the small scale desalination apparatus installed in the bookstore according to the embodiment of the present invention has been exemplified that the operation data and the sensor data are remotely collected from the small scale desalination apparatus 110 through the virtual private network (VPN) , But is not limited thereto.

The optimal operation parameter determination unit 130 determines an optimum flux and a recovery rate corresponding to the remote collected operation data for optimum operation of the small scale desalination apparatus 110 based on a model predictive controller. Hereinafter, a detailed description of the optimum operating parameter determination will be described later with reference to FIG. 3 to FIG.

The process index monitoring unit 140 monitors the water permeability coefficient and the relative permeability index for the process diagnosis of the small scale desalination apparatus 110 to provide a warning function for the membrane pollution degree.

The membrane replacement time predicting unit 150 predicts the replacement timing of the reverse osmosis membrane module corresponding to the membrane contamination degree using the artificial neural network model. Here, a detailed description of the process index monitoring and the separation membrane replacement timing prediction will be described later with reference to FIGS. 8 to 11. FIG.

The remote diagnosis management system 100 of the small scale desalination apparatus installed in the bookstore according to the embodiment of the present invention is a system in which a plurality of small scale seawater or nacelle desalination apparatuses 110 are installed and distributed by a virtual private network (VPN) The optimization operation flux and the recovery rate can be determined, and the separation membrane replacement time can be remotely diagnosed.

Specifically, FIG. 2 is a view illustrating an example of collecting sensor data and operation data from the small-scale desalination apparatus shown in FIG. 1 to determine optimal operating parameters and predicting a membrane replacement timing.

2, the small scale desalination apparatus 110 includes an inflow water supply pump 111, an inflow water sensor 112, a double filter medium filter (DMF) 113, a prefilter 114, a high pressure pump 115, Module 116, a separation membrane sensor 117, an energy recovery unit (ERD 118) and a production water tank 119, wherein the influent water sensor 112 comprises a water temperature sensor, a Total Dissolved Solid (TDS) But are not limited to, sensors and quantity sensors.

The process management and control unit 120 may include a process information input unit 121 and a water quality and quantity monitoring unit 122 in a remote diagnosis management system 100 of a small scale desalination apparatus distributed on a book paper according to an embodiment of the present invention. have.

The process information input unit 121 inputs process configurations according to the separation membrane type and separation membrane connection conditions as process information according to the operation data collected for the small-scale desalination apparatus 110.

The water quality and quantity monitoring unit 122 measures the influent water temperature, the concentration (TDS) of total dissolved solids, the concentration and the quantity of boron collected from the influent water sensor 112 of the small-scale desalination apparatus 110, .

The optimum operating parameter determiner 130 determines an optimum flux and a recovery rate as optimal operating parameters based on a model predictive controller. Specifically, the optimum operating parameter determiner 130 determines the optimal diffusion model of the reverse osmosis module 116 based on the solution diffusion material transfer model of the reverse osmosis module 116 of the small-scale desalination apparatus 110, And operation control is performed so as to maintain the operation flux and the recovery rate determined based on the operation flux.

Particularly, the solution diffusion mass transfer model is calculated by considering the mass transfer coefficient of the reverse osmosis membrane, considering the temperature correction of the influent water, the osmotic pressure and the pressure loss per membrane, and including the concentration polarization index at the membrane surface. In addition, the optimization model predictive controller may limit the concentration of the total dissolved solids, the treated water quality for the harmful substances of boron and the power consumption consumed in the small-scale desalination apparatus 110, ) Determines the optimum flux and recovery rate after determining the recovery rate and the optimum range of the flux according to these limiting conditions, and finally determines the pressure of the high-pressure pump which is the operating variable.

Meanwhile, the remote diagnosis management system 110 of the small scale desalination apparatus installed on the book paper according to the embodiment of the present invention considers the influent water quality, the production quantity and the mass transfer characteristic of the separation membrane of the book paper in which the small-scale desalination apparatus 110 is distributed The water quality and the power consumption are irregular due to the irregular recovery rate generated by operating the high-pressure pump 115, which is a driving variable in the state where the high-pressure pump 115 is not operated. Accordingly, It is necessary to solve the problem that the volatility becomes so difficult.

FIG. 3 is a schematic diagram of a dissolution diffusion mass transfer model of the reverse osmosis membrane of the small-scale desalination apparatus shown in FIG. 2, and FIG. 4 is a model predictive controller-based optimum flux for a reverse osmosis module of the small- Fig. 5 is a diagram illustrating an algorithm for determining a high-pressure pump operating pressure.

는 온도보정을 하도록 한다. 다음으로, 도 4에 도시된 바와 같이, 하기 수학식 3에 의해 유입수 성상에 기초한 삼투압을 계산하고, 하기 수학식 4는 역삼투 분리막에 의한 압력손실을 반영한다. In the dissolution diffusion mass transfer model of the reverse osmosis membrane shown in FIG. 3, the flux of solution mass transfer and the initial solution transfer coefficient are given by the following equations (1) and (2), respectively. At this time, the mass transfer coefficient

Lt; / RTI > Next, as shown in FIG. 4, the osmotic pressure based on the influent water phase is calculated by the following equation (3), and the following equation (4) reflects the pressure loss due to the reverse osmosis membrane.

) 및 회수율(

)을 의미한다.Next, Equation (5) is characterized in that the concentration polarization formed on the surface of the reverse osmosis membrane is considered in the embodiment of the present invention, and the mass transfer coefficient in the concentration polarization model is calculated by Equation (6). Also, the power consumption, which is a limiting variable for calculating the optimum flux and the recovery rate, is calculated by the following equation (8) by the optimization model predictive controller of the optimum operating parameter determiner 130. [ Equations (7) and (9) are obtained by repeatedly calculating the above equations (1) to (8) by a recursive algorithm and calculating the final flux

) And recovery rate

).

는 다음의 수학식 1과 같이 구할 수 있다.Specifically, the flux of Solvent Transport (unit: m / s)

Can be obtained by the following equation (1).

는 용액 전달계수(Solvent transport parameter: 단위는 m/s-Pa)를 나타내며,

는 유입수측 압력(Feed Pressure to RO system: 단위는 Pa)을 나타내고, 그리고

는 분리막내 압력손실(Pressure loss in an RO system: 단위는 Pa)을 나타낸다.here,

Represents the solution transport parameter (unit: m / s-Pa)

Represents the feed pressure to the RO system (unit: Pa), and

Indicates the pressure loss in an RO system (unit: Pa).

)는 다음의 수학식 2와 같이 주어진다.In addition, the solution transfer coefficient for the solvent transfer (Solvent Transport)

) Is given by the following equation (2).

는 초기 용액 전달계수(Solvent transport parameter: 단위는 m/s-Pa)를 나타내며,

는 분리막 막면적(㎡)을 나타내고,

은 총 분리막 막면적(㎡)을 나타내며,

는 온도(℃)를 나타내고,

는 회수율을 나타내며,

은 점성계수를 나타내고, 그리고

는 유입수측 압력을 나타낸다.here,

Represents the initial solution transfer parameter (unit: m / s-Pa)

(M < 2 >),

Represents the total membrane area (m < 2 >),

Represents the temperature (占 폚)

Represents the recovery rate,

Represents the viscosity coefficient, and

Represents the inflow side pressure.

(단위는 Pa)는 다음의 수학식 3과 같이 주어진다.Next, the osmotic pressure of the reverse osmosis membrane,

(Unit Pa) is given by the following equation (3).

은 기체상수(Gas constant: 단위는 J/mol-K)를 나타내고,

는 수온(단위는 K)을 나타내며,

는 분리막 표면상 용질농도(Solute concentration at membrane surface: 단위는 mol/㎥)를 나타내고, 그리고

는 생산측 용질농도(Solute concentration at permeate side: 단위는 mol/㎥)를 나타낸다.here,

Is a gas constant (unit: J / mol-K)

Represents the water temperature (unit: K)

Represents the solute concentration at the membrane surface (unit: mol / m < 3 >), and

Indicates the solute concentration at the permeate side (unit: mol / m 3).

는 다음의 수학식 4와 같이 주어진다.Next, the pressure drop in an element of the reverse osmosis membrane,

Is given by the following equation (4).

는 동수구배(Hydraulic gradient: 단위는 m)를 나타내고,

및

는 각각의 분리막 종류별 상수를 나타내며,

는 농축수측의 채널내 교차흐름 속도(Crossflow velocity in an RO brine channel: 단위는 m/s)를 나타내고, 또한,

는 확산속도(단위는 m/s)를 나타낸다. 이때, 상기 동수구배란 배관의 수직 고저차(m)를 배관의 길이(m)로 나눠준 값을 말한다.here,

Represents the hydraulic gradient (unit: m)

And

Represents a constant for each membrane type,

Represents the crossflow velocity in an RO brine channel (unit: m / s) of the concentrated water side,

Represents the diffusion rate (unit: m / s). In this case, the above-mentioned hydraulic gradient refers to a value obtained by dividing the vertical height difference (m) of the pipe by the length (m) of the pipe.

Next, the concentration polarization is given by the following equation (5).

는 표면상 용질농도(Solute concentration at membrane surface: 단위는 mol/㎥)를 나타내고,

는 투과측 용질농도(Solute concentration at permeate side: 단위는 mol/㎥)를 나타내며,

는 농축수측의 용질농도(Solute concentration at brine side: 단위는 mol/㎥)를 나타내고,

는 물질전달상수(Mass transfer coefficient: 단위는 m/s)를 나타내며, 그리고

는 플럭스를 나타낸다.here,

Represents the solute concentration at the membrane surface (unit: mol / m < 3 >),

Represents the solute concentration at the permeate side (unit: mol / m < 3 >),

Indicates the solute concentration at the brine side (unit: mol / m < 3 >),

Indicates the mass transfer coefficient (unit: m / s), and

Represents the flux.

(단위는 m/s)는 다음의 수학식 6과 같이 주어진다.Specifically, the mass transfer coefficient (Mass Transfer Coefficient)

(Unit: m / s) is given by Equation (6) below.

는 농축수측의 채널내 교차흐름 속도(단위는 m/s)를 나타내고,

는 용질 확산속도 상수(Solute diffusion coefficient: 단위는 ㎡/s)를 나타내며,

는 농축수측의 용질농도(Solute concentration at brine side: 단위는 mol/㎥)를 나타내고,

는 동수구배(단위는 m)를 나타내며,

는 비중(Density)을 나타내며,

는 확산속도를 나타낸다.here,

Represents the cross-channel flow rate (unit: m / s) of the concentrated water side,

Represents the solute diffusion coefficient (unit: m 2 / s)

Indicates the solute concentration at the brine side (unit: mol / m < 3 >),

Represents the same water gradient (unit: m)

Represents the density,

Represents the diffusion rate.

은 다음의 수학식 7과 같이 주어진다.On the other hand, the recovery ratio

Is given by the following Equation (7).

는 유입수량(Feed flow rate: 단위는 ㎥/s)을 나타내고,

는 생산수량(Permeate flow rate: 단위는 ㎥/s)을 나타낸다.here,

Represents the feed flow rate (unit: m < 3 > / s)

The Permeate flow rate (unit: m3 / s).

(단위는 kWh/㎥)는 다음의 수학식 8과 같이 주어진다.Next, the specific energy

(Unit: kWh / m < 3 >) is given by Equation (8).

는 유입수량(Feed flow rate: 단위는 ㎥/s)을 나타내고,

는 농축수 유량(Brine flow rate: 단위는 ㎥/s)을 나타내며,

는 생산수량(Permeate flow rate: 단위는 ㎥/s)을 나타내고,

는 펌프 효율(단위는 %)을 나타내며,

는 ERD 효율(단위는 %)을 나타내고,

는 유입수측 압력(단위는 Pa)을 나타내며, 그리고

는 농축수 압력(단위는 Pa)을 나타낸다.here,

Represents the feed flow rate (unit: m < 3 > / s)

Represents the concentration of water (brine flow rate: unit: m 3 / s)

Represents the production flow rate (unit: m3 / s)

Represents the pump efficiency (unit is%),

Represents the ERD efficiency (unit:%)

Represents the inflow side pressure (unit Pa), and

Represents the concentrated water pressure (unit: Pa).

(단위는 ㎥/㎡-hr)는 다음의 수학식 9와 같이 주어진다.On the other hand, the average flux (Average Flux)

(Unit: m3 / m < 2 > -hr) is given by the following equation (9).

는 분리막의 막 면적(단위는 ㎡)을 나타내고,

는 생산수량(Permeate flow rate: 단위는 ㎥/s)을 나타내고,

은 n번째 분리막의 플럭스를 나타내며,

은 총 분리막의 수를 나타내고, 그리고

은 총 분리막 막면적을 나타낸다.here,

Represents the membrane area (unit: m2) of the separation membrane,

Represents the production flow rate (unit: m3 / s)

Represents the flux of the n-th separator,

Represents the total number of membranes, and

Represents the total membrane area.

5 illustrates a simulation of a program for determining a model predictor controller-based optimum flux and a high-pressure pump operating pressure of a small-scale desalination apparatus in a remote diagnosis management system of a small-scale desalination apparatus installed on a book paper distributed on a book paper according to an embodiment of the present invention 6 is a diagram illustrating a result of a reverse osmosis optimization of a program for determining a model predictive controller-based optimum flux and a high-pressure pump operation pressure of a small-scale desalination apparatus, and Fig. 7 is a model predictive controller- Flux and the recovery rate of the catalyst.

As shown in FIGS. 5 and 6, the optimal operation flux and recovery rate determined by the algorithm considering the power consumption and the limiting variables of the produced water quality (for example, boron) are presented in the small-scale desalination apparatus 110, For example, in the case of boron, an average of 3-5 mg / L exists in seawater.

However, as shown in FIG. 6, in the case of boron, the boron is not effectively removed in the reverse osmosis (RO) process, even though it is a very harmful substance to the human body. Since the membrane is installed with a single-pass structure, it is necessary to pay more attention to the removal of harmful substances.

As shown in FIG. 7, when the power consumption is 2.5 kWh / m 3 and the boron concentration is 1 mg / L, the optimum operation condition is 18 L / m 2 -hr at 12.5 for flux, 40%. The program described above can be installed on the spot where the small scale desalination device 110 is installed and can also be monitored and operated by the designer, the installer and the person responsible for the operation and maintenance of the program installed at the remote site have.

Referring again to FIG. 2, the separation membrane replacement timing predicting unit 150 for the reverse osmosis module 116 will be described as follows.

The separation membrane replacement timing predicting unit 150 predicts the water permeation coefficient of the reverse osmosis membrane of the small-scale desalination apparatus 110 dispersed in a large number of dispersed water by using Equation 2 described above and water temperature, TDS, And an algorithm for evaluating replacement timing of artificial neural network model-based reverse osmosis membranes, which predicts the degree of membrane contamination considering influent and concentrated water pressure (TMP), flow rate, and operation time.

According to the separation membrane replacement timing predicting unit 150, it is possible to remotely sense the degree of film contamination and the time for replacing the membrane, and to supply stable and safe water so that the book can be stopped without interruption.

Also, the membrane replacement time predicting unit 150 may calculate the contamination of the separation membrane by calculating the algorithms of the artificial neural network model as shown in Equations (10) and (11) below and predict the contamination degree and replacement timing of the reverse osmosis membrane , And a specific configuration diagram is as shown in Fig.

8 is a diagram for explaining prediction of a process anomaly alarm and a membrane replacement time of a small-scale desalination apparatus based on an artificial neural network model in a remote diagnosis management system of a small scale desalination apparatus installed on a book paper according to an embodiment of the present invention And FIG. 9 is a diagram illustrating structures of the input layer, the hidden layer, and the output layer of the artificial neural network model for predicting the replacement time of the membrane of the small-scale desalination apparatus shown in FIG. 10 is a diagram illustrating a configuration of an artificial neural network model implemented by a program for predicting a replacement time of a membrane of a small scale desalination apparatus in a remote diagnosis management system of a small scale desalination apparatus installed in a book paper according to an embodiment of the present invention And FIG. 11 is a diagram illustrating a result of prediction of the membrane replacement time according to the artificial neural network model shown in FIG.

8, the separation membrane replacement time predicting unit 150 includes an artificial neural network model 151, a feedback unit 152, and a separation membrane replacement timing determination unit 153. The artificial neural network model 151, The feedback unit 152 includes a model input unit 151-1, an artificial neural network model processing unit 151-2 and a model output unit 151-3. 1 and a feedback control unit 152-2, and the water transmission coefficient calculated by the water transmission coefficient calculation unit 141 is input to the model input unit 151-1. 9, the artificial neural network model includes an input layer, a hidden layer, and an output layer, and inputs water temperature, TDS, TMP, flow rate, and operation time to the input layer, Can be output.

는 입력(x1, x2, …, xp)에 대한 가중합을 나타내고,

는 바이어스(Bias)를 의미하며,

는 출력 결과를 나타낸다.

는 i번째 입력과 j번째 뉴런사이의 연결강도를 나타내고,

는 j번째 뉴런의 활성화 함수이다. 특히,

는 비선형 함수로서, 여기서는 시그모이드 함수를 적용하기로 한다. 이는 비선형 함수인 시그모이드 함수를 적용하여, 결정 영역이 통상의 직선이 아니라 완만한 곡선으로 경계를 형성시킴으로써 행위의 분석이 복잡하지만 미분을 통해 은닉층을 학습할 수 있도록 하기 위함이다. 인공신경망모델은 예측값 및 실제값을 비교하여 오차가 작은 방향으로 노드간의 연결강도(

)를 다음의 수학식 12를 이용하여 조절하고, 출력값을 출력한다. here,

Represents the weighted sum for the inputs (x1, x2, ..., xp)

Quot; means a bias,

Represents the output result.

Represents the connection strength between the i-th input and the j-th neuron,

Is the activation function of the jth neuron. Especially,

Is a nonlinear function, and a sigmoid function is applied here. This is to apply a sigmoid function, which is a nonlinear function, so that the decision domain is not a normal straight line but a gentle curve, thereby forming a boundary, so that the behavior analysis is complex, but the hidden layer can be learned through the derivative. The artificial neural network model compares the predicted value and the actual value,

) By using the following expression (12), and outputs the output value.

는 p번째 목표패턴의 j 성분이고,

는 p번째 입력패턴에서 신경망 모델이 계산한 출력패턴의 j 성분이며,

는 p번째 입력패턴의 i 성분이고,

는

로서, 목표패턴과 실제패턴의 차(오차)를 나타내며, 또한,

는 점성계수를 나타낸다.here,

Is the j component of the p-th target pattern,

Is the j component of the output pattern calculated by the neural network model in the p-th input pattern,

Is the i-th component of the p-th input pattern,

The

(Error) between the target pattern and the actual pattern,

Represents the viscosity coefficient.

9, the artificial neural network model applied to the remote diagnosis management system 100 of the small-scale desalination apparatus installed in the booklet according to the embodiment of the present invention includes a multilayered structure having a hidden layer between the input layer and the output layer, Structure learning using a multi-layer perceptron that includes an algorithm for training data in a structure, and can be implemented by a program as shown in FIGS. 10 and 11. FIG.

Here, the parameters of the input layer are water temperature, TDS, pressure difference (TMP), water flow rate and operation time of the concentrated water side, and final permeability coefficient of the output layer. In this artificial neural network model, when the input condition is input to the input layer, the operation result performed by the node in the hidden layer becomes the input value of the node at the next level, and this process is performed to the output layer to derive the final result. In this case, it is the connection strength to connect the nodes in each layer. This connection can not connect nodes in the same layer, but can connect nodes in different layers, and usually one node is connected to all nodes in the next layer.

)를 다음의 수학식 13과 같이 연산하여, 이를 막 오염도 및 교체시기를 예측할 수 있다.In the artificial neural network model, each node can be modeled as an artificial neuron. Each neuron sums the input external stimulus and responds to the result. Through the artificial neural network model processing unit 151-2 deduced as the artificial neural network model, the water permeability coefficient is calculated from the relative water permeability index (

) Can be calculated according to the following Equation (13), and it is possible to predict the degree of contamination of the membrane and the replacement timing.

는 용액전달계수(Solvent transport parameter: 단위는 m/s-Pa)를 나타내고,

는 초기 용액전달계수(Solvent transport parameter: 단위는 m/s-Pa)를 나타낸다.here,

Represents the solution transport parameter (unit: m / s-Pa)

Represents the initial solution transfer parameter (unit: m / s-Pa).

)를 산출하여 초기 대비 물 투과능의 공정능력지수가 초기 투과도로부터 30% 이상 지속적으로 감소하는 경우, 공정 생산능력에 이상이 발생하였다는 경고를 발생하고, 이후, 분리막 교체시기 결정부(153)는 상기 인공신경망모델에 의하여 계산된 값이 사용자의 기준 값에 도달하는 경우, 분리막 교체시기로 판단하여 분리막 교체시기 정보를 상기 소규모 담수화장치(110)의 관리자에게 제공할 수 있다.At this time, the relative water permeability index (

If the process capability index of the water permeability is continuously decreased by 30% or more from the initial permeability, a warning is issued that an abnormality has occurred in the process production capacity, When the value calculated by the artificial neural network model reaches the user's reference value, it is determined that the membrane is to be replaced and the membrane replacement timing information is provided to the manager of the small-scale desalination apparatus 110.

As a result, the main features implemented in the remote diagnosis management system 100 of the small scale desalination apparatus installed in the booklets according to the embodiment of the present invention are as follows.

First, in the case of a small scale desalination plant using a reverse osmosis process for seawater or wastewater treatment, most of it is operated by a non-expert in operating flux conditions determined by the designer without conducting site water quality testing and test testing, A small scale desalination apparatus 110 installed in a large number distributes a plurality of small scale desalination apparatuses 110 installed in a small scale desalination apparatus in which a plurality of distributed small scale desalination apparatuses 110 are installed in a field control unit 110 including an algorithm for determining optimal fluxes and recovery rates Or it can be monitored and monitored at a remote site.

Second, in the case of a remote diagnosis management system 100 of a small scale desalination apparatus installed on a book paper according to an embodiment of the present invention, mass transfer coefficient is monitored through a mass transfer model of salt ions and water ions in water, The long-term replacement time can be predicted by considering the contamination degree of the membrane due to the influence of various membrane pollutants by using the artificial neural network algorithm considering influent water temperature, total dissolved solids concentration, have.

Third, in the case of the remote diagnosis management system 100 of the small scale desalination apparatus installed in the bookstore according to the embodiment of the present invention, data collection of a plurality of distributed small scale desalination apparatuses 110 is minimized and stored in a standardized database structure And a data processing structure for distributing data to a central server at a remote site at predetermined time intervals and collecting data according to a predetermined rule and distributing data to the distributed small desalination apparatuses 110. The data processing structure can be monitored at a remote site at all times. Therefore, it is possible to easily detect the process diagnosis of the small-scale desalination apparatus installed at a remote place and the replacement time of the membrane in advance.

[Remote Diagnosis Management Method of Small-scale Water Treatment System Installed on the Book]

12 is a flowchart illustrating an operation of a remote diagnosis management method for a small scale desalination apparatus installed in a book on a book according to an embodiment of the present invention.

Referring to FIG. 12, a remote diagnosis management method for a small scale desalination apparatus installed on a book paper according to an embodiment of the present invention includes: first, a small-scale desalination apparatus 110 distributed on a book, Lt; RTI ID = 0.0 > (S110). ≪ / RTI >

Next, the process management and control unit 120 collects sensor data and operation data from each of the small-scale desalination apparatuses 110 through a virtual private network (VPN) (S120).

Next, the membrane type and process configuration are input according to the operation data of the reverse osmosis process of the small-scale desalination apparatus 110 (S130). That is, as the process information according to the operation data collected for the small-scale desalination apparatus 110, a process configuration according to the separation membrane type and separation membrane connection conditions is input.

Next, the water quality and the water quality are monitored according to the sensor data collected from the influent water sensor 112 of the small-scale desalination apparatus 110 (S140). At this time, the temperature of the influent water collected from the influent water sensor 112 of the small scale desalination apparatus 110, the concentration (TDS) of total dissolved solids, the concentration and the quantity of boron are measured and converted into a database.

Next, the optimal operation parameter determination unit 130 determines the flux and the recovery rate, which are optimum operating variables for the optimal operation of the small-scale desalination apparatus 110, based on the model predictive controller (S150). Specifically, the optimum operating parameter determiner 130 determines the optimal operating parameter based on the solution diffusion material transfer model of the reverse osmosis membrane of the small-scale desalination apparatus 110 and the operation flux determined based on the optimization model predictive controller- And the recovery rate is maintained. At this time, the solution diffusion material transfer model considers the mass transfer coefficient for each type of reverse osmosis membrane, takes into consideration the temperature correction of the influent water, osmotic pressure and pressure loss per membrane, The optimization model predictive controller sets the concentration of the total dissolved solids or boron in the treatment water for the harmful substances and the power consumption consumed in the small scale desalination apparatus 110 as the limiting condition, The optimum operating parameter determiner 130 may determine the recovery rate and the optimum range of the flux Determining the optimum flux and the recovery rate after determining, and can finally determine the pressure of the high-pressure pump operating parameters.

)를 산출하여 초기 대비 물 투과능의 공정능력 지수가 초기 투과도로부터 30% 이상 지속적으로 감소하는 경우, 공정 생산능력에 이상이 발생하였다는 경고를 발생할 수 있다.Next, the process index monitoring unit 140 diagnoses and monitors the process of the small-scale desalination apparatus 110 by calculating and comparing the water permeability coefficient and the relative permeability index corresponding to the separation membrane pollution degree (S160). At this time, the process index monitoring unit 140 continuously calculates the water permeability coefficient, and calculates the relative water permeability index (

), And if the process capability index of the water permeability from the beginning is continuously decreased by 30% or more from the initial permeability, a warning may be generated that an abnormality occurs in the process production capacity.

Next, the separation membrane replacement timing predicting unit 150 predicts the separation membrane replacement timing corresponding to the separation membrane contamination degree using the artificial neural network model (S170). The separation membrane replacement time predicting unit 150 may determine the degree of contamination of the separation membrane and the replacement timing by using the artificial neural network model without interruption of operation of the small scale desalination apparatus 110, The input parameters of the artificial neural network model of the separation membrane replacement time predicting unit 150 may include water temperature, total dissolved solids (TDS), pressure on the inflow side of the separation membrane and pressure on the concentrated water side (TMP), flow rate and run time, and the output factor may be the water permeability coefficient.

As a result, according to the embodiment of the present invention, a small-scale desalination apparatus including a microfiltration filter, a reverse osmosis membrane, and other facilities for seawater or nacelle desalination processing distributed in domestic and overseas papers, Based on the optimization model of the dissolved diffusion mass transfer model and the operating parameters (flux and recovery rate) to explain the phenomenon, the optimal operating parameters are determined at the site or remote site considering the water quality and quantity of the place where the small desalination device is installed, It is possible to minimize the variability.

According to the embodiment of the present invention, the water permeability coefficient and the relative permeability index are monitored for process diagnosis to provide a warning function for the membrane pollution degree to the driver, and the time for replacing the membrane is detected in advance by the artificial neural network model, It is possible to supply stable and safe water supply so that it can be endless.

According to an embodiment of the present invention, it is possible to provide a manager who has to provide quality management for design, construction and operation for a small-scale seawater or an aquarium desalination apparatus installed in several tens to thousands of booklets, .

According to embodiments of the present invention, data obtained from a water quality sensor such as simplified process information (membrane type and connection configuration), flow rate and total dissolved solids (TDS), water temperature, Flux and recovery rates can be computed and communicated to the site remotely or reflected on the driver's decision in the field.

According to the embodiment of the present invention, it is possible to determine the reverse osmosis membrane contamination degree using the temperature-compensated water permeability coefficient and the relative permeability index, and to predict the membrane replacement time based on the artificial neural network model.

According to the embodiment of the present invention, it is possible to monitor the self-diagnosis and remote-phase operation state of a plurality of distributed small-scale desalination apparatuses.

It will be understood by those skilled in the art that the foregoing description of the present invention is for illustrative purposes only and that those of ordinary skill in the art can readily understand that various changes and modifications may be made without departing from the spirit or essential characteristics of the present invention. will be. It is therefore to be understood that the above-described embodiments are illustrative in all aspects and not restrictive. For example, each component described as a single entity may be distributed and implemented, and components described as being distributed may also be implemented in a combined form.

The scope of the present invention is defined by the appended claims rather than the detailed description and all changes or modifications derived from the meaning and scope of the claims and their equivalents are to be construed as being included within the scope of the present invention do.

100: Remote diagnosis management system of small scale water treatment system
110: Small scale desalination device (seawater or water desalination device)
120: process control and control unit 130: optimal operation variable determining unit
140: process index monitoring unit 150: separation membrane replacement timing predicting unit
111: Influent supply pump 112: Influent sensor
113: Dual filter media (DMF) 114: Prefilter
115: high pressure pump 116: reverse osmosis module
117: Membrane sensor 118: Energy recovery device (ERD)
119: Production water tank 121: Process information input unit
122: water quality and quantity monitoring part 141: water permeability coefficient calculating part
151: Neural network model unit 152: Feedback unit
153: Separator replacement timing determining unit 151-1: Model input unit
151-2: Neural network model processing unit 151-3: Model output unit
152-1: feedback control section 152-2: water permeability coefficient error calculation section

Claims (18)

A small-scale desalination device 110 distributed on a book and having a reverse osmosis (RO) system for desalinating seawater or nursery water by a reverse osmosis module 116;
A process management and control unit 120 for remotely collecting operation data and sensor data from the distributed small-scale desalination apparatus 110, inputting corresponding process information, and monitoring the quality and quantity of the small-scale desalination apparatus;
An optimal operation variable determining unit 130 for determining an optimal flux and a recovery rate corresponding to the remote collected operation data for optimum operation of the small scale desalination apparatus 110 based on a model predictive controller;
A process index monitoring unit 140 for monitoring the water permeability coefficient and the relative permeability index to provide a warning function of the membrane pollution degree for the process diagnosis of the small scale desalination apparatus 110; And
And a membrane replacement timing predicting unit 150 for predicting the replacement timing of the reverse osmosis module membrane corresponding to the membrane contamination degree using the artificial neural network model,
The process management and control unit 120 includes a process information input unit 121 for inputting process configurations according to separation membrane type and separation membrane connection conditions as process information according to operation data collected for the small scale desalination apparatus 110; And a water quality and quantity monitoring unit 122 for measuring the influent water temperature, the concentration of dissolved solids (TDS), the concentration and the quantity of boron collected from the influent water sensor 112 of the small-scale desalination apparatus 110, A remote diagnosis management system of a small scale desalination device installed on a book site containing delete The method according to claim 1,
The optimal operation parameter determiner 130 determines the operation flux and the recovery rate based on the optimization model predictive controller based on the solution diffusion material transfer model of the reverse osmosis membrane of the small scale desalination apparatus 110 and the process analysis simulation according to the operating variables The remote diagnosis management system of the small-scale desalination apparatus installed in the booklet. The method of claim 3,
Considering the mass transfer coefficients of the reverse osmosis membranes and taking into consideration the temperature correction, osmotic pressure and pressure loss of each membrane, the optimum solution parameter determiner 130 determines the concentration polarization index And a remote diagnosis management system for a small-scale desalination apparatus installed on a book. The method of claim 3,
The optimization model predictive controller of the optimum operation parameter determiner 130 restricts the concentration of the total dissolved solids or the treated water quality for the harmful substances of boron and the power consumption consumed in the small scale desalination apparatus 110, The optimal operating parameter determination unit 130 determines the optimum flux and the recovery rate after determining the recovery rate and the optimal range of the flux according to the restriction conditions, and finally determines the pressure of the high-pressure pump as the operating variable. Remote Diagnosis Management System of Distributed Small Scale Desalination Unit. The method according to claim 1,
The process index monitoring unit 140 continuously calculates the water permeability coefficient and calculates the relative water permeability index (

), And when the process capability index of the water permeability from the initial value is continuously decreased by 30% or more from the initial permeability, a warning is issued that an abnormality has occurred in the process production capacity, and a small scale desalination device Remote diagnostic management system. The method according to claim 6,
The separation membrane replacement time predicting unit 150 predicts that the value calculated by the artificial neural network model reaches the reference value of the user after a warning that an abnormality has occurred in the process production capacity from the process index monitoring unit 140 The remote control system of the small scale desalination apparatus is provided with the separation membrane replacement timing information to the manager of the small scale desalination apparatus 110 by determining that the separation membrane is to be replaced. The method according to claim 1,
The separation membrane replacement time predicting unit 150 may determine the degree of contamination of the separation membrane and the timing of replacement by the artificial neural network model without stopping the operation of the small scale desalination apparatus 110, A remote diagnosis management system for a small-scale desalination apparatus installed in a library. 9. The method of claim 8,
The artificial neural network model input factors of the membrane replacement time predicting unit 150 are water temperature, total dissolved solids (TDS), pressure difference (TMP) on the inflow side and concentrated water side of the membrane, flow rate and operation time, Wherein the parameter is a water permeability coefficient; and the remote diagnosis management system of the small-scale desalination apparatus installed on the book. The method according to claim 1,
The optimum operation parameter determiner 130 and the membrane replacement time predicting unit 150 are implemented in a field controller of a book magazine to determine the operating conditions of the optimum flux and high pressure pump by themselves, A remote diagnosis management system of a small-scale desalination apparatus installed on a book site, which is characterized by self-diagnosis together with an alarm. a) a small-scale desalination apparatus 110 dispersed on a book, each producing fresh water by reverse osmosis with seawater or nose as an influent;
b) the process control and control unit 120 collects sensor data and operation data from each of the small-scale desalination apparatuses 110 via a virtual private network (VPN);
c) inputting a membrane type and a process configuration according to operational data of the reverse osmosis process of the small scale desalination apparatus (110);
d) monitoring water quality and quantity according to the sensor data collected from the influent water sensor (112) of the small scale desalination device (110);
e) determining an optimum operating parameter determining unit 130 based on a model predictive controller and determining a flux and a recovery rate, which are optimum operating parameters for the optimum operation of the small scale desalination apparatus 110;
f) diagnosing and monitoring the process of the small scale desalination apparatus 110 by calculating and comparing the water permeability coefficient and the relative permeability index corresponding to the separation membrane pollution degree by the process index monitoring unit 140; And
g) estimating the separation membrane replacement time predictor 150 using the artificial neural network model to predict the separation membrane replacement time corresponding to the separation membrane contamination degree,
The process management and control unit 120 of the step b) may include a process information input unit for inputting a process configuration according to a separation membrane type and a separation membrane connection condition as process information according to operation data collected for the small scale desalination unit 110, (121); And a water quality and quantity monitoring unit 122 for measuring the influent water temperature, the concentration of dissolved solids (TDS), the concentration and the quantity of boron collected from the influent water sensor 112 of the small-scale desalination apparatus 110, A method for remote diagnosis management of a small-scale water treatment apparatus distributed on a book including a plurality of books. delete 12. The method of claim 11,
In step e), the optimum operation parameter determiner 130 determines the optimum operation of the small desalination apparatus 110 based on the solution diffusion material transfer model of the reverse osmosis separator 110, Wherein the operation control is performed so as to maintain the flux and the recovery rate. 14. The method of claim 13,
Considering the mass transfer coefficients of the reverse osmosis membranes and taking into consideration the temperature correction, osmotic pressure and pressure loss of each membrane, the optimum solution parameter determiner 130 determines the concentration polarization index And a remote diagnosis management method of a small-scale water treatment apparatus distributed on a book. 14. The method of claim 13,
The optimization model predictive controller of the optimum operation parameter determiner 130 restricts the concentration of the total dissolved solids or the treated water quality for the harmful substances of boron and the power consumption consumed in the small scale desalination apparatus 110, The optimum operating parameter determiner 130 determines the optimum flux and the recovery rate after determining the recovery rate and the optimal range of the flux according to the restriction conditions, and finally determines the pressure of the high-pressure pump as the operating variable. A method for remote diagnosis management of installed small water treatment devices. 12. The method of claim 11,
The process index monitoring unit 140 of the step f) continuously calculates the water permeability coefficient and calculates the relative water permeability index

), And when the process capability index of the water permeability from the initial value is continuously decreased from the initial permeability by more than 30%, a warning is issued that an abnormality has occurred in the process production capacity. Of remote diagnosis management. 12. The method of claim 11,
In step g), the separation membrane replacement timing predicting unit 150 determines the degree of contamination and replacement timing of the separation membrane by the artificial neural network model without stopping operation of the small-scale desalination apparatus 110, And remote diagnosis and management of the device (110). 18. The method of claim 17,
The artificial neural network model input factors of the membrane replacement time predicting unit 150 are water temperature, total dissolved solids (TDS), pressure difference (TMP) on the inflow side and concentrated water side of the membrane, flow rate and operation time, Wherein the factor is the water permeability coefficient.

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