KR102325991B1 - Intelligent asset management system for wastewater treatment facilities and the operation method using it - Google Patents

Abstract

The present invention relates to a wastewater treatment facility maintenance system and, more specifically, to an intelligent asset management system for wastewater treatment facilities which measures states of facilities through measurement elements such as power, insulation resistance, hearing sound, and the like by an algorithm and applies them to an intelligent digital measuring device, thereby maintaining the facilities.

Description

Intelligent asset management system for wastewater treatment facilities and the operation method using it

The present invention relates to an intelligent asset management system and operation for sewage and wastewater treatment facilities, and more particularly, by measuring the state of the facility with an intelligent digital instrument through measurement elements such as power, insulation resistance, and sound, and linking with water quality, quantity, etc. Thus, it relates to a method of finding and operating the optimal operating conditions.

Korea Patent No. 2174722, which is a conventional invention, is when a mechanical device such as a motor or a pump is newly installed in a field such as a water purification plant or a sewage treatment plant, the data collected from the motor or pump in the field is absolutely insufficient. After installation, big data is obtained by applying different processing and learning methods to the data collected during the trial operation period (t0) and the normal operation period (t1) after the trial operation period ends, and using this, field machines such as motors and pumps are used. It relates to an instrumentation control system that predicts failures by machine learning data received from on-site mechanical devices that can improve the failure prediction performance of devices.

Korea Patent No. 2176467 prevents environmental pollution accidents in advance and improves productivity and product quality by monitoring each device in a manufacturing or processing facility that can pollute the environment in real time so that an accident can be dealt with in advance. It relates to a pollution source emission process facility management system using IoT that enables improvement, and is installed in pollution prevention facilities that treat pollutants discharged from pollution source discharge process facilities that discharge wastewater or gas that can pollute the environment A process facility management system, comprising: a sensor module comprising at least one of a concentration sensor, a differential pressure sensor, a flow rate sensor, a temperature sensor, a vibration sensor, and a current sensor according to a type of pollutant discharged from a pollution prevention facility; a field controller having a wireless communication module to collect each signal detected by the sensors of the sensor module and transmit it to the following environment monitoring server; an environment monitoring server equipped with reference value information to determine whether there is an abnormality in comparison with the information transmitted from the on-site controller, and notify the abnormality when an abnormality occurs; And it characterized in that it comprises a mobile terminal for receiving the abnormal situation transmitted from the environment monitoring server.

However, in the above-described conventional inventions, it is difficult to precisely process an unstable output value due to a malfunction or malfunction, and the repair cost is greatly increased because it is not possible to prevent a malfunction and protect the equipment.

Korean Patent No. 2174722 Korean Patent No. 2176467 Korea Registered Patent No. 1987897 Korean Patent No. 1729932 Korean Patent No. 1501749

The present invention has been made to solve the above problems, and the present invention measures factors such as power, insulation resistance, and hearing in the facilities of a sewage treatment facility, and analyzes the amount of change compared to the management standard value of the measurement element to determine whether there is an abnormality It aims to provide an intelligent asset management system for sewage and wastewater treatment facilities that can do this.

Another object of the present invention is to provide an asset management and operation method based on autonomous operation of a sewage wastewater treatment facility that is remotely controlled using unmanned automation and IoT technology by linking data such as water quality and quantity of the sewage treatment facility.

In order to solve the above problems, the present invention provides a water treatment facility sensor module including a water quantity sensor, a water quality sensor, a water level sensor, and a concentration sensor; And power (including voltage, current, etc.), insulation resistance, a diagnostic measurement sensor module including a hearing sensor; A data transmission unit for collecting and transmitting data from the sensor modules to the upper level and easily processing diagnostic analysis, and different processing and learning methods for the collected data to secure big data, and manage the collected data a diagnostic monitoring server that receives and stores reference value information, determines whether there is an abnormality by machine learning the information, and notifies the abnormality when an abnormality occurs; and a mobile terminal for receiving the abnormal occurrence status transmitted from the diagnostic monitoring server.

The water treatment facility sensor module and the diagnostic measurement sensor module are IoT sensors.

The diagnostic measurement sensor module analyzes the amount of change compared to the management standard value of the measurement element based on the measurement data, analyzes it according to the risk and importance of the facility, and predictive maintenance and energy management to determine whether to maintain or replace it based on this. .

In the present invention, it is possible to compare and monitor the current operating point and the optimal operating point by tracking the change in the performance curve as the pump is used in the performance curve that is diagrammed after the pump is manufactured and tested.

The present invention made as described above enables asset management and autonomous operation by linking water quality, quantity, and facility condition in the treatment process.

In addition, the present invention can prepare for future population reduction and avoidance of working in environmental facilities through autonomous driving.

In addition, the present invention enables facility asset management, such as maintenance priority or replacement time, through the life (endurance) of the device (facility).

In addition, the present invention can maximize the operating efficiency of the facility by finding the optimum operating efficiency point (finding and operating the intersection of operating conditions between the best water quality and the best facility condition).

In addition, the present invention can establish scientific data-based design standards (prevention of overdesign) rather than the existing theoretical design standards.

In addition, the present invention allows comparison of design standards and current operating conditions (using management standards).

In addition, the present invention can predict the amount of greenhouse gas reduction and power reduction through the operation data of the treatment facility.

감축량 등을 활용)을 찾을 수 있다.In addition, the present invention provides an optimal process and method (equipment price, power consumption,

reduction, etc.) can be found.

In addition, the present invention can determine whether a failure or malfunction by measuring the power, insulation resistance, hearing sound, etc. of various measuring devices in a sewage treatment plant, and prevents malfunction of various devices and protects facilities against unstable output values due to failure and malfunction can

In addition, according to the present invention, various IoT sensors capable of detecting leaks of pollutants or aging conditions of facilities are installed in each part of process equipment in a sewage treatment plant, so that detection is possible at all times.

In addition, the present invention can provide the effect of providing higher reliability to the process performance monitoring system of the sewage treatment facility by providing predictive information of the process performance of the sewage treatment facility with improved accuracy.

In addition, the present invention can perform real-time remote monitoring and, when a problem occurs, automatically notify the supervisor and the person concerned, and take immediate action on its own.

In addition, the present invention can predict failure patterns of motors and pumps in sites such as water purification plants or sewage treatment plants.

1 is a view showing the overall configuration of an intelligent asset management system for sewage and wastewater treatment facilities according to an embodiment of the present invention.
2 is a view showing a configuration in which an intelligent asset management system (intelligent predictive maintenance) for sewage and wastewater treatment facilities is combined with an existing operating system according to an embodiment of the present invention.
2 is a view showing a water treatment facility sensor module, a diagnostic measurement sensor module, a data transmission unit, a diagnostic monitoring server, and the like according to an embodiment of the present invention.
3A and 3B are diagrams illustrating a method of comparing an amount of power according to a water level, power, etc. with a reference value according to an embodiment of the present invention.
3C is a diagram illustrating an example of data accumulation and extraction according to another embodiment of the present invention.
3D is a diagram illustrating an example of data extraction through calculation of deviation according to another embodiment of the present invention.
3E is a diagram illustrating an example of tracking a correction coefficient according to another embodiment of the present invention.
3F is a diagram illustrating an example of extraction of false data according to another embodiment of the present invention.
FIG. 3G is a diagram illustrating an example of tracking a target value of a correction measurement value through a correction slope according to another embodiment of the present invention.
3H is a diagram illustrating an example of data removal through correction according to another embodiment of the present invention.
4 is a view showing analysis using a diagnostic measurement sensor and a water treatment facility sensor according to another embodiment of the present invention.
5 is a view showing the priority of maintenance or replacement based on the state of the facility using the intelligent digital measuring instrument according to another embodiment of the present invention according to the degree of risk and importance.
6 is a diagram illustrating an analysis of a current operating state by monitoring a pump performance curve according to another embodiment of the present invention.

In order to fully understand the present invention, preferred embodiments of the present invention will be described with reference to the accompanying drawings. Embodiments of the present invention may be modified in various forms, and the scope of the present invention should not be construed as being limited to the embodiments described in detail below. This example is provided to more completely explain the present invention to those of ordinary skill in the art. Accordingly, the shape of elements in the drawings may be exaggerated to emphasize a clearer description. It should be noted that the same members in each drawing are sometimes shown with the same reference numerals. In addition, detailed descriptions of well-known functions and configurations that are determined to unnecessarily obscure the gist of the present invention will be omitted.

1A and 1B, the present invention includes measurement sensor modules 101 and 102, a data transmission unit 200, a diagnostic monitoring server 300, and the like.

The measuring device includes: a diagnostic measurement sensor module 101 for measuring a power measurement value, an insulation resistance value, and a mechanical sound of a mechanical device in the field; and a water treatment facility sensor module 102 comprising at least one of water treatment sensors including a water quantity sensor, a water quality sensor, a water level sensor, and a concentration sensor.

The data transmission unit 200 is a module that collects data from measuring instruments and transmits data to a diagnostic monitoring server for diagnostic analysis of the data.

The diagnostic monitoring server 300 accumulates and stores the water level and power values according to a predetermined period of data collected from the measurement sensor modules 101 and 102, and removes data deviating from the stored cluster by more than a predetermined value, and the same water level Each value is corrected through a correction coefficient based on It is a module that generates a water level-power relationship curve after calculation, and notifies this when an abnormality is detected through the water level-power relationship curve.

As an embodiment, the measurement sensor modules 101 and 102 may include: a water treatment facility sensor module comprising at least one of water treatment sensors including a water quantity sensor, a water quality sensor, a water level sensor, and a concentration sensor; and a diagnostic measurement sensor module for measuring the power measurement value, insulation resistance value, and machine sound of a mechanical device in the field, wherein the water treatment facility sensor module and the diagnostic measurement sensor module are IoT sensors.

The diagnostic monitoring server 300 analyzes the amount of change compared to the management reference value of the measurement element based on the measurement data of the diagnostic measurement sensor module 101 and the water treatment facility sensor module 102, so that predictive maintenance and energy management of the facility is possible. do.

The diagnostic monitoring server 300 secures big data by applying different data processing and learning methods collected from the measuring instrument, receives and stores the collected data and reference value information, and determines whether there is an abnormality by machine learning the information and , when an error occurs, it is notified to the manager, etc. As such machine learning, EWMA (Exponential Weighted Moving Average), which gives higher weight to recent data, is used, and lifespan data analysis can be performed by applying the Weibull function to Bayesian statistical techniques.

The management baseline for modeling the management baseline of the diagnosis and monitoring server 300 is defined as a reference by accumulating data obtained when the equipment was tested in the best condition when the equipment was just installed.

As shown in FIG. 2, the system to which the intelligent digital measuring instrument is applied has a diagnostic monitoring server 300 for intelligent predictive maintenance in the existing operating system, various water treatment facility sensor modules 102, and the diagnostic measurement sensor module 101. Focusing on the values, the general motor receives the power value, in the case of a transfer pump motor in water, the power value and insulation resistance value, and the important motor that is judged to be a core facility receives information such as hearing sound and MCSA and temporarily executes it according to a specific algorithm. It is possible to display <power vs. water level> values according to

As shown in the graph of FIG. 3c, if June 2000: (Management Standard) When the facility is just installed, power data according to the water level for one month is accumulated and displayed as a graph.

In other words, the manager can see how much the condition of the facility has changed compared to the 'management standards' in July of 2010/2020 and June of 2000.

The intelligent digital measuring instrument can select a representative value of information measured by the water treatment facility sensor module 102 and the diagnostic measurement sensor module 101 and set a diagnostic reference value using a two-dimensional algorithm.

Here, the X-axis represents water level, flow rate, and pressure, and the Y-axis represents the amount of power (effective/reactive), power factor, THD (voltage, current), efficiency, load factor, insulation resistance, etc.

In detail, as shown in FIG. 3A , the step of accumulating and storing the water level or power value according to a predetermined period (S11); removing data deviating from the stored water level or power value group by more than a certain value (S12); correcting each water level or power value through a correction coefficient based on the same water level (S13); a management baseline modeling step (S14) after calculating a representative value by accumulating initial operation data after installation among the corrected water level or power values; and generating a water level-power relationship curve after calculating a representative value by accumulating recent data among the corrected water level or power values (S15).

accumulating and storing the water level or power value according to the predetermined period (S11); The step (S12) of removing the data deviating from the stored water level or power value group by more than a certain value; proceeds from step S101 to step S106 of FIG. 3B, and each level or power value through a correction factor based on the same water level correcting (S13); After accumulating initial operation data after installation among the corrected water level or power values and calculating a representative value, it is preferable that the management baseline modeling step (S14) and the like proceed to step S107 of FIG. 3B .

Specifically, the correcting step (S13) is a data correction and representative value extraction method through tracing a correction coefficient according to a variance value, and includes a data extraction step through calculation of a difference between the water level or power value and an average; and tracing the correction coefficient to increase accuracy by setting the correction coefficient less than a predetermined value for the values with the large deviation and increasing the correction coefficient for the values with the small deviation more than the predetermined value when the variance is large.

After generating the water level-power relationship curve (S15), data correction step when the measured value of power consumption is lower than the management standard of the management reference line due to error data; and tracking the target value of the corrected measurement value through the correction slope of the data correction step.

The data correction step may include: calculating a management reference slope and a measurement value slope in each section and correcting the measurement value for the error data; re-calculating the measured value through the slope correction corresponding to the correction; If the measured power consumption value is lower than the management standard even after the re-calculation (correction), determining that the data is erroneously measured and removing the data; consists of, etc.

Referring to the drawings, as shown in FIG. 3D , data correction through tracing a correction coefficient according to a 'dispersion' value and data extraction through calculation of a deviation as a representative value extraction method are represented by Equation 1 below.

That is, the deviation (average-value) x(P) is the power x(V) - the power average (3.818) x(P), and the correction value F(P) is calculated as the power x(V) * K (correction factor) do.

As shown in Fig. 3e, the standard of tracking the correction coefficient as a data correction and representative value extraction method through tracing the correction coefficient according to the 'dispersion' value is through Equation 2 below, the lower the absolute value of the deviation, the lower the correction coefficient (Km ) is increased, and when the variance is large, the interval of the correction coefficient between each data is greatly adjusted. When the variance is large, the correction coefficient is reduced as much as possible for the values with a large deviation, and the correction coefficient is increased for the values with a small deviation to increase the accuracy.

Referring to the example of FIG. 3E, as shown in Equation 3 below, the number of data n, the interval of the correction coefficients = m, and the sum of the correction coefficients = 1, and when m is selected, the smallest number among the correction coefficients is substituted for calculation.

As shown in Figure 3f, in the process of extracting erroneous data in the detection of abnormality in the motor through the water level-power curve slope correction, if the current measured value - control standard value > reference value, it is normal, and if the current measurement value - control standard value <= reference value, it is abnormal / Caution is required.

In general, power consumption is higher than the management standard due to performance degradation over time, and data correction is required when the measured power consumption value is lower than the management standard due to error data.

Although the water level-power curve is taken as an example in FIG. 3f , the X-axis represents the water level and the Y-axis represents the efficiency. In general, in the case where the value does not rise and falls, contrary to the power over time, the opposite can be applied. .

As shown in Fig. 3g, with reference to Equation 4 below, the target value tracking of the correction measurement value through the correction slope in the detection of motor abnormality through the water level-power curve slope correction is the control standard slope and the measured value slope in each section. The process of correcting the measured value for the error data by calculation, recalculating the measured value through slope correction, and determining the data as erroneously measured data and removing the data if the measured power consumption value is lower than the management standard even after re-calibration (correction) made by

Looking at the example of Figure 3h, if you look at the △ control standard (control standard slope), 0 is calculated by calculating the control standard difference/measured value representative difference (13.5kw - 13.5kw)/(13kw-14kw) at the water level 2.2 m, △Measured value (measured value slope) is calculated by calculating the measured value (representative) difference/water level difference (13kw-14kw)/(2.2m-2m), and -5 is obtained.

If this is corrected based on the numerical value obtained in this way, 11.95kW lower than the control standard can be recorded at 3.0m water level, and data with △control and △measured values below a certain value can be removed.

As shown in FIG. 4, the intelligent digital measuring instrument compares the reference value and the current value of the flow/pressure, water level, power, etc., which are the measurement values of the water treatment facility sensor module 102 and the diagnostic measurement sensor module 101, and analyzes the pattern. have.

As shown in FIG. 5, the water treatment facility sensor module 102 for sensing the sensor value of the water treatment facility for each process in the intelligent digital measuring instrument, and the diagnostic instrumentation sensor module 101 for measuring the motor characteristic values such as power, insulation resistance, and hearing sound ), it is possible to graph the importance and risk level of the information collected in the algorithm formula to manage them in red, orange, yellow, etc. have.

As shown in FIG. 6, the intelligent digital measuring instrument checks the optimal operating conditions for each motor for high-efficiency energy operation according to water level and pressure among the information collected from the water treatment facility sensor module 102 and the diagnostic measurement sensor module 101 and sewage treatment plant Operational algorithms can be provided.

Therefore, it is possible to compare and monitor the current operating point and the optimal operating point by tracing the change in the performance curve as the pump is used in the performance curve that is diagrammed after the pump is manufactured and tested.

101: diagnostic measurement sensor module
102: water treatment facility sensor module
200: data transmission unit
300: diagnostic monitoring server

Claims (8)

a data transmission unit that collects data from intelligent digital measuring instruments and water treatment instruments and transmits data to a diagnosis monitoring server for diagnostic analysis of the data;
The data collected from the intelligent digital measuring instrument and the water treatment instrument are accumulated and stored according to a certain period of time, and data deviating from the stored cluster by more than a certain value are removed, and each value through a correction factor based on the same water level by accumulating the initial operation data after installation among the corrected values, calculating the representative value, and then modeling the management baseline, and accumulating the most recent data among the corrected values to calculate the representative value and then generating a water level-power relationship curve, , a diagnostic monitoring server that notifies when an abnormality is detected through the water level-power relationship curve;
The diagnostic monitoring server receives the water level or power value from the data transmission unit according to a certain period, accumulates and stores it, removes data that deviate by more than a certain value from the stored water level or power value cluster, and uses a correction factor based on the same water level. After correcting each water level or power value, calculating a representative value by accumulating initial operation data after installation among the corrected water level or power values, and accumulating recent data among the corrected water level or power values to calculate the representative value create a power relationship curve,
The correction is a data correction and representative value extraction method through the diagnosis monitoring server tracking correction coefficients according to variance values, and the diagnosis monitoring server extracting data through the calculation of a difference between the water level or power value and the mean deviation; Sewage and wastewater treatment facility facility intelligent asset management for tracking correction coefficients that increase accuracy by lowering the correction coefficient less than a certain value and raising the correction coefficient for the values with a large deviation above a certain value when the variance is large system. The method according to claim 1,
The measuring device includes: a water treatment facility sensor module comprising at least one of water treatment sensors including a water quantity sensor, a water quality sensor, a water level sensor, and a concentration sensor;
A sewage and wastewater treatment facility facility, comprising a; a diagnostic measurement sensor module for measuring the power measurement value, insulation resistance value, and machine sound of the on-site mechanical device, wherein the water treatment facility sensor module and the diagnostic measurement sensor module are IoT sensors Intelligent asset management system. 3. The method according to claim 2,
Sewage and wastewater treatment facility intelligent asset management system, characterized in that predictive maintenance and energy management of facilities are possible by analyzing the amount of change compared to the management standard value of the measurement element based on the measurement data of the water treatment facility sensor module and the diagnostic measurement sensor module. The method according to claim 1,
The diagnosis monitoring server secures big data by applying different data processing and learning methods collected from the measuring instrument, receives and stores the collected data and reference value information, then performs machine learning to determine whether there is an abnormality, and notifies the abnormality when an abnormality occurs. Sewage and wastewater treatment facility intelligent asset management system, characterized in that. In the method using the intelligent asset management system of the sewage and wastewater treatment facility of claim 1,
receiving, by the diagnosis monitoring server, a water level or power value according to a predetermined period to a data transmission unit, and accumulating and storing the received;
removing, by the diagnostic monitoring server, data that deviate from the stored water level or power value group by more than a predetermined value;
correcting, by the diagnosis monitoring server, each water level or power value through a correction coefficient based on the same water level;
a management baseline modeling step after the diagnosis monitoring server accumulates initial operation data after installation among the corrected water level or power values and calculates a representative value;
generating, by the diagnostic monitoring server, a water level-power relationship curve after calculating a representative value by accumulating recent data among the corrected water level or power values;
The correcting step is
As a data correction and representative value extraction method through the diagnosis monitoring server tracking correction coefficients according to variance values,
data extraction step by the diagnostic monitoring server by calculating a difference between the water level or power value and the average;
a tracking step of the diagnostic monitoring server, when the variance of the values with the large deviation is large, reducing the correction coefficient less than a certain value and raising the correction coefficient for the values with the small deviation more than the predetermined value to increase the accuracy. Sewage and wastewater treatment facility intelligent asset management method, characterized in that. delete 6. The method of claim 5,
After generating the water level-power relationship curve,
a data correction step, by the diagnostic monitoring server, when the measured power consumption value is lower than the management standard of the management reference line due to error data;
Sewage and wastewater treatment facility intelligent asset management method comprising a; the diagnostic monitoring server tracking the target value of the corrected measurement value through the correction slope of the data correction step. 8. The method of claim 7,
The data correction step is
performing, by the diagnosis monitoring server, a correction of the measurement value for the error data by calculating the slope of the management reference and the slope of the measurement value in each section;
re-calculating, by the diagnosis monitoring server, a measured value through tilt correction corresponding to the correction;
Sewage and wastewater treatment facility intelligent asset management method comprising a; when the power consumption measurement value is lower than the management standard even after the recalculation, by the diagnostic monitoring server, as erroneously measured data and removing the data.

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