KR102557961B1 - Method and apparatus for managing atmospheric environment pollutants of factories by using internet of things sensors and big data - Google Patents

Abstract

The present invention relates to a method for managing atmospheric environment pollutants of factories by using Internet of things (IoT) sensors and big data, which may improve safety and thus reduce accidents. The method for managing atmospheric environment pollutants of factories by using IoT sensors and big data comprises: receiving time-sequentially accumulated database (DB) information, which includes measurement information on atmospheric environment pollutants leaked from fugitive emission factory facilities and information related to safety and environment, from the IoT sensors; predict the amount of the leakage of the atmospheric environment pollutants based on the DB information; and send a first alarm signal, which notifies that the leaked atmospheric environment pollutants may cause environmental damage, to a manager terminal when the predicted leakage amount exceeds a predetermined threshold leakage amount.

Description

Method and device for managing atmospheric pollutants in workplaces using IoT sensors and big data

The present invention relates to a method and apparatus for managing atmospheric pollutants in a workplace, and more particularly, to a method and apparatus for integrated management of scattered emissions of atmospheric pollutants in a workplace based on Internet of Things (IoT) sensors and big data.

In general, a fugitive emission source refers to a pollutant that discharges pollutants into the air without passing through a point emission source such as a chimney or an incinerator among air pollution emission sources. Fugitive emissions are also accidental releases of vapors or gases from pressurized equipment due to equipment failure, leaks or unexpected accidents. These fugitive emissions can occur through evaporation in places such as storage tanks or wastewater treatment facilities, and although not limited to industrial environments, the majority of fugitive emissions occur in factories, power plants, and workplaces in various industries.

Particularly in power plants, steel mills, cement, smelters, and waste recycling plants, the possibility of fugitive emissions is very high, so it can be very difficult to find and correct all problems. In addition, fugitive emissions can often represent a surprisingly large proportion of air pollutants emitted from workplaces, which can be very dangerous to the environment and to humans.

Piping, pumps, compressors, pressure safety valves (PSVs), pressure relief valves (PRVs), flanges, sampling connections, connectors, open lines, process drains, agitators, etc. installed in most industrial processes require regular inspection and maintenance. In addition, facilities and devices that are sources of fugitive emissions from various work procedures, chemicals and the above devices used in processes such as handling, manufacturing, production, and storage, and devices and facilities capable of fugitive emissions and leakage are major management targets.

The LDAR (Leak Detection and Repair) management system is a system that effectively manages air pollutants, volatile organic compounds (VOCs), and hazardous air pollutants (HAPs) leaking from fugitive emission sources located in various unit processes such as production, inventory, processing, storage, shipment, and cleaning, process discharge facilities, and treatment facilities. In addition, the LDAR management system comprehensively considers the locations, device types, operating conditions, and design conditions of facilities with potential for leaks in all facilities in the workplace, monitors the operation status of process facilities to ensure effective maintenance management, ensures regular facility inspections and repairs for emissions and leaks, and informationizes the database (Data Base) to enable continuous monitoring, prevention, and maintenance, and furthermore, leak information is inspected and managed to prepare for emissions and leaks to a certain level. can be minimized or prevented.

On the other hand, laws and regulations on atmospheric pollutants, VOCs, and HAPs of the government and the Ministry of Environment are gradually being strengthened, and corporate social responsibility for environmental safety issues is becoming more important and public interest in the environment is heightened. As a result, interest and effort in corporate environmental safety issues are becoming very important in improving corporate image and value.

Accordingly, it is necessary to establish measures to reduce fugitive emissions from fugitive emission facilities, and to measure and report fugitive emissions. As ESG (Environment, Social, & Governance), sustainable management and eco-friendliness are becoming indicators of corporate value, a system for integrated management of fugitive emissions of pollutants in the workplace is needed.

In addition, industrial processes in various industries are composed of various facilities and devices, and chemical substances may leak and scatter in the form of gas, liquid, gas, and liquid two-phase due to damage to these devices and facilities. This can cause chemical accidents, environmental damage, and human casualties. Therefore, a management system is required to prevent this.

The present invention proposes a method and apparatus for integrated management of fugitive emissions of atmospheric pollutants in workplaces based on IoT sensors and big data.

In addition, the present invention accumulates a database for the maintenance of fugitive emission facilities that are concerned about fugitive emissions at workplaces and industrial plants based on information on the flow rate, pressure, temperature, flow rate, concentration, etc. of atmospheric pollutants leaking from fugitive emission facilities, and converts time-series information into big data to predict and grasp fugitive emissions in advance. Through this, fugitive emissions of air pollutants can be reduced to prevent chemical accidents, improve safety, and reduce the occurrence of safety accidents. Integrated management of fugitive emissions We propose a method and apparatus.

In addition, the present invention is a solution capable of managing air pollutants suitable for the emission characteristics of workplaces, and provides basic data necessary for legal regulations in the field of environment / safety / health, improves environmental performance by effectively making and managing decisions, reduces air pollution and cleans the atmospheric environment, and proposes an integrated management method and apparatus for fugitive emissions that can reduce environmental risks and prevent or mitigate chemical accidents to reduce costs and improve productivity.

In the method according to an embodiment of the present invention, in the method for managing air pollutants using IoT sensors and big data, the IoT sensor includes a process of receiving DB information accumulated over time, including measurement information on air pollutants leaking from unorganized emission workplace facilities and information related to safety and the environment, a process of estimating the amount of leakage of the air pollutants based on the DB information, and notifying that the leaked air pollutants may cause environmental damage if the predicted leakage exceeds a predetermined threshold leakage amount. includes transmitting the first alert signal to the manager terminal.

An apparatus according to an embodiment of the present invention, in an apparatus for managing air pollutants using an IoT sensor and big data, includes a prediction module for receiving DB information accumulated over time, including measurement information on air pollutants leaking from unorganized emission workplace facilities and information related to safety and environment from the IoT sensor, and predicting the amount of leakage of the air pollutant based on the received DB information, and when the predicted leakage exceeds a predetermined threshold leakage, the leaked air pollutant may cause environmental damage and an alerting module for transmitting a first alerting signal to the manager terminal.

The present invention provides an atmospheric environment management method and device using an IoT sensor, and based on information on the flow rate, pressure, temperature, humidity, flow rate, concentration, etc. of atmospheric pollutants leaking from fugitive emission facilities, statistical analysis data and prediction models are used to accumulate maintenance matters of fugitive emission facilities that are concerned about fugitive emissions at workplaces and industrial plants as a database, and time-series information can be converted into big data to predict and grasp in advance, thereby reducing fugitive emissions of air pollutants and accidents caused by chemical accidents It has the effect of reducing accidents by preventing accidents and improving safety.

In addition, as a solution that can manage pollutants in the air environment suitable for the emission characteristics of the workplace, it provides basic data necessary for legal regulations in the field of environment / safety / health, improves environmental performance by effectively making and managing decisions, and effectively acts in reducing air pollution and cleaning the atmospheric environment, reducing environmental hazards, and preventing or mitigating chemical accidents, thereby reducing costs and improving productivity.

1 is a diagram showing an air environment management system according to an embodiment of the present invention;
2 is a diagram showing the signal flow of the air quality management system according to an embodiment of the present invention;
Figure 3 is a flow chart showing the operation of the central management server in the air quality management system according to an embodiment of the present invention;
Figure 4 is a device diagram showing the internal configuration of the central management server related to the air quality management system 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. In addition, it should be noted that in the following, only parts necessary for understanding operations according to embodiments of the present invention will be described, and descriptions of other parts will be omitted so as not to distract from the subject matter of the present invention. In addition, terms to be described later are terms defined in consideration of functions in the embodiments of the present invention, which may vary according to the intention or custom of a user or operator. Therefore, the definition should be made based on the contents throughout this specification.

Since the present invention can make various changes and have various embodiments, it will be described in detail by exemplifying specific embodiments in the drawings. However, this is not intended to limit the present invention to specific embodiments, and should be understood to include all modifications, equivalents, and substitutes included in the spirit and scope of the present invention.

In addition, unless otherwise defined in the embodiments of the present invention, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning in the context of the related art, and unless explicitly defined in the embodiments of the present invention, an ideal or excessively formal meaning. It is not to be interpreted.

1 is a diagram illustrating an air environment management system according to an embodiment of the present invention.

Referring to FIG. 1 , the air quality management system according to an embodiment of the present invention includes a central management server 100, an IoT sensor 110, and a manager terminal 120.

The IoT-based IoT sensor 110 is attached to various unit processes, process facilities, and handling facilities in the workplace, such as pipes, pumps, compressors, PSVs, PRVs, flanges, sampling connections, connectors, open lines, process drains, agitators, etc., and periodically or time-sequentially measures atmospheric pollutants leaking from the fugitive emission facilities and converts them into DB information.

Although not shown, the IoT sensor 110 is connected to a DB server through an IoT network, and the DB server collects measurement information received from a plurality of IoT-based sensors to accumulate a DB. The accumulated DB is transmitted to the central management server 100 through the IoT sensor 110 (step 112). The accumulated DB includes a safety-related information DB and an environment-related information DB. The safety-related information DB is composed of, for example, the Hazardous Substances List DB of the Occupational Safety and Health Act, the Hazardous Chemicals List DB of the Chemical Substances Control Act, hazardous substances or hazardous chemical handling facilities or equipment DB, power facility DB, piping facility DB, valve DB, tank and storage facility DB. It consists of a device DB.

Here, fugitive emission facilities or devices that need to be managed periodically may be, for example, piping, pumps, compressors, PSVs, PRVs, flanges, sampling connections, connectors, open lines, process drains, agitators, etc. A device for handling chemicals that are scattered and discharged in an open state may be, for example, a chemical handling tank supplied to a film calendering device.

The central management server 100 acquires information on the flow rate, pressure, temperature, humidity, flow rate, concentration, etc. of atmospheric pollutants leaking from the fugitive emission facility based on the DB information received from the IoT sensor 110, and applies the obtained information to a multivariable correlation prediction equation to predict the amount of leakage of air pollutants. The central management server 100 may generate big data for time-series information by accumulating information on the amount of leakage of predicted atmospheric pollutants, and apply artificial intelligence neural networks and deep learning by long short term memory (LSTM) to the big data and DB. Here, deep learning means constructing a deep neural network by using multiple hidden layers when creating a model from data. The central management server 100 can automatically learn all of these deep learning models using a learning material DB, and through this, it is possible to actively prevent air pollutant discharge facilities from leaking.

Hereinafter, the central management server 100 applies a multivariate correlation prediction formula based on the DB information received from the IoT sensor 110 to predict the amount of leakage of air pollutants to build big data. It will be described in detail. The amount of leakage of the atmospheric pollutant can be largely predicted by dividing it into a case of leakage from a chemical facility and a case of leakage from a pipe, and depending on the state of leakage, different model formulas, that is, the following mathematical model formula among various model formulas. In addition, the term mathematical formula used in this specification is used in the same meaning as a mathematical model formula. The leaking state may be one of a gas or vapor state, a liquid state, or a flashing liquid-vapor state. In particular, when leaking in a gas or vapor state, the leak rate may be additionally considered to calculate the leak amount.

- In case of leakage from chemical installations

One) In case of gas or vapor leakage

The central management server 100 calculates the critical flow pressure ratio and compares the leak velocity with the sonic velocity to predict the leak amount. The critical flow pressure ratio (P _CF /P ₁ ) can be calculated as in Equation 1 below.

Here, P _CF represents the critical flow pressure (kg _f / cm ² , lb _f / cm ² ), P ₁ represents the operating pressure of the chemical plant (kg _f / cm ² , lb _f / cm ² ), and γ represents the specific heat coefficient (C _P /C _V ).

Then, when the leak rate is greater than the speed of sound (P _a /P ₁ ≤ P _CF /P ₁ ), the leak amount Q can be predicted as shown in Equation 2 below.

Here, Q represents the leakage amount (kg/sec, lb/sec), _CD represents the leakage coefficient (dimensionless), g _c represents the gravitational constant (9.8 kg m/kg _f sec ² , 32.2 lb ft/lb _f sec ² ), M _W represents the molecular weight (kg/kg-mole, lb/lb-mole), A represents the leak source area (m ² , ft ² ), and T1 represents the operating temperature (K, R) of the chemical facility, and R represents the gas constant (847 m kg _f /kg-mole K, 1,545 ft lb _f /lb-mole R).

The leak source area can be calculated or assumed as follows depending on the leak cause. In the case of leakage due to cracks or damages in chemical facilities other than transfer or compression facilities, the leak source area is calculated as the area of a hole through which all hazardous substances handled/stored in the facility to be treated may leak within 10 minutes.

In the case of leakage due to pipe cracks or ruptures, the leak source area is assumed as the cross-sectional area of the pipe if the nominal diameter of the pipe is less than 50 mm, the cross-sectional area of the 50 mm pipe if the nominal diameter of the pipe is between 50 mm and 100 mm, and the cross-sectional area corresponding to 20% of the pipe cross-sectional area if the nominal diameter of the pipe exceeds 100 mm.

In the case of leakage due to cracks or damage of conveying or compression equipment, the area of the leakage source is calculated as the area specified in the preceding paragraph according to the size of the suction pipe. In the case of leaks caused by valve opening due to mismanagement, the leak source area is calculated as the area of the full bore of the valve. In the case of leakage due to emergency discharge, the area of the leak source is calculated as the area by the inner diameter of the emergency discharge pipe.

The leakage coefficient C _D can be represented as shown in Table 1 below.

When the leak rate is less than the speed of sound (P _a /P ₁ >P _CF /P ₁ ), the leak amount Q can be predicted as shown in Equation 3 below.

where P _a represents the atmospheric pressure (kg _f /cm ² , lb _f /ft ² ).

2) In case of liquid leakage

The central management server 100 may predict the leakage amount Q as shown in Equation 4 below.

Here, ρ _L represents the density of the leaking hazardous substance (kg/m ³ , lb/ft ³ ), g represents the gravitational acceleration (9.8 m/sec ² , 32.2 ft/sec ² ), and h represents the liquid height difference between the leak point and the chemical facility (m, ft).

3) In case of liquid-vapor leakage

Leakage of a two-phase fluid occurs when the liquid flashes into a vapor state due to a difference in pressure as the liquid leaks, and can be classified into a case where a saturated liquid leaks and a case where a subcooled liquid leaks. In addition, when the saturated liquid leaks, the case where the equilibrium saturated liquid corresponds to the case where the leaking point is 0.1 m or more from the outside of the chemical facility and the leaking point corresponds to the case where the leaking point is within 0.1 m from the outside of the chemical facility. It is divided into cases where nonequilibrium saturated liquid leaks.

The central management server 100 determines the ratio of steam generated when the two-phase fluid leaks.

It is calculated as in Equation 5.

where f _v represents the flash fraction generated, Represents the average static pressure specific heat of the liquid (kcal / kg K, Btu / lb R), Tb represents the boiling point (K, R) of the liquid under atmospheric pressure, represents the average latent heat of vaporization (kcal/kg, Btu/lb).

Then, when the equilibrium saturated liquid leaks, the leakage amount Q can be predicted as shown in Equation 6 below.

here Represents the latent heat of vaporization at operating temperature (kcal/kg, Btu/lb), ρ _G represents the vapor density at operating pressure (kg/m ³ , lb/ft ³ ), K represents a constant (427 m kg _f /kcal, 778 ft lb _f /Btu), represents the specific heat (kcal/kg·K, Btu/lb·R) of the liquid at the operating temperature.

When a non-equilibrium saturated liquid leaks, the leakage amount Q can be estimated as shown in Equation 7 below.

Here, N can be expressed as in Equation 8 below.

Here, L _P represents the pipe length (m, ft) from the outer surface of the chemical facility to the point of leakage (less than 0.1 m), and L _e represents the experimental constant (0.1 m, 0.33 ft).

When the supercooled liquid leaks, the leak amount Q can be estimated as shown in Equation 9 below.

Here, P _v represents the vapor pressure (kg _f / cm ² , lb _f / ft ² ) at the operating temperature, and Q _s is calculated by Equation 6 when L _P ≥ 0.1 m, and calculated by Equation 7 when L _P <0.1 m.

- In case of leakage from the pipe

One) In case of gas or vapor leakage

The central management server 100 calculates the pipe friction coefficient, the Mach number, the critical flow pressure ratio, and the gas leakage temperature, and then predicts the leakage amount. The pipe friction coefficient can be calculated as in Equation 10 below.

Here, f represents the coefficient of friction (dimensionless), ε represents the roughness coefficient of the pipe (m, ft), and D represents the inner diameter of the pipe (m, ft).

The roughness according to the material of the pipe is shown in Table 2 below.

The Mach number can be calculated by a trial and error method using Equation 11 below.

Here, Ma represents the Mach number (dimensionless), LP represents the pipe length (m, ft) from the outer surface of the chemical facility to the damaged part, and D represents the inner diameter (m, ft) of the pipe.

The critical flow pressure ratio (P _CF /P ₁ ) can be calculated as in Equation 12 below.

Here, P _CF represents the critical flow pressure (kg _f / cm ² , lb _f / cm ² ), P ₁ represents the operating pressure of the chemical plant (kg _f / cm ² , lb _f / cm ² ), and γ represents the specific heat coefficient (C _P /C _V ).

In addition, the central management server 100 may calculate the gas leakage temperature by a trial and error method using Equation 13 below.

Here, T means the gas leakage temperature (K, R).

Then, when the gas flow rate is greater than the speed of sound (P _a /P ₁ ≤ P _CF /P ₁ ), the leakage amount Q can be predicted as shown in Equation 14 below.

When the flow rate of the gas is less than the speed of sound (P _a /P ₁ >P _CF /P ₁ ), the leakage amount Q can be predicted as shown in Equation 15 below.

2) In case of liquid leakage

The central management server 100 calculates the friction coefficient as shown in Equation 16 below and then predicts the leak amount.

Here, Re represents the Reynolds number (dimensionless), and μL represents the viscosity of the liquid (kg/cm·sec, lb/ft·sec).

Then, in the case of laminar flow, i.e. When ≤ 180, the leakage amount Q can be predicted as shown in Equation 17 below.

In the case of turbulent flow, i.e. When ≥ 525, the leakage amount Q can be predicted as shown in Equation 18 below.

3) In case of liquid-vapor leakage

It can be classified into a case where the saturated liquid leaks and a case where the supercooled liquid leaks. In the case where the saturated liquid leaks, the leakage amount Q can be predicted as shown in Equation 19 below.

where F represents the flow reduction coefficient (dimensionless). At this time, the flow rate reduction coefficient can be represented as shown in Table 3 below.

Based on the DB information received from the IoT sensor 110 in this way, the central management server 100 predicting the amount of leakage of atmospheric pollutants through Equations 1 to 19 compares the predicted leakage amount with a predetermined threshold leakage amount. As a result of the comparison, if the predicted leakage amount exceeds the critical leakage amount, the central management server 100 transmits an alarm signal to the manager terminal 120 indicating that the currently leaking air pollutant may cause environmental damage.

Upon receiving the alarm signal, the terminal in charge of environmental management, the terminal in charge of environment, and the terminal of the chief environmental decision maker take appropriate measures to prevent environmental damage to the corresponding fugitive emission facility (step 116), thereby preventing the emission of air pollutants and reducing fugitive emissions.

In addition, the central management server 100 predicts the leakage damage distance, that is, the end point of the leakage damage radius, based on the leakage amount of air pollutants and the area of the leakage source. The endpoint of the leak damage radius can be predicted by dividing the leaked atmospheric pollutant into a case where it is an inflammable material and a case where it is a toxic material.

When the leaked air pollutant is an inflammable substance (gas or liquid), the central management server 100 predicts the distance the inflammable substance reaches, that is, the end point of the leakage damage radius, thereby predicting a fire or explosion accident caused by the inflammable substance in advance. In addition, the central management server 100 confirmed the management standards of the workplace and transmits an alarm signal to the administrative terminal 120 that informs the administrator 120 that the current leaking flammable material may cause safety accident damage when the above predicted endpoint exceeds the predetermined critical endpoint of the management criteria (step 114). It may be a crystal terminal, and the central management server 100 transmits the alarm signal to each of these terminals.

When the leaked air pollutant is a toxic substance, the central management server 100 measures the distance at which the toxic substance diffuses into the atmosphere and affects, that is, the endpoint concentration of the leak damage radius, using a Gaussian diffusion model, a Gaussian plume model, and a Gaussian purple model. In addition, the central management server 100 checks the management criteria of the workplace, and if the measured endpoint concentration exceeds the predetermined threshold endpoint concentration of the management criteria, it determines that the currently leaking toxic substance may cause safety accident damage, and transmits an alarm signal to the administrator terminal 120 to inform the manager terminal 120 to take appropriate measures to prevent safety accident damage (step 114). ) transmits the alert signal to each of these terminals.

At this time, the application priority of the endpoint concentration (mg/l or ppm) standard is as follows. As a first priority, the Emergency Response Planning Guideline 2 (ERPG2) announced by the American Industrial Hygiene Association (AIHA) is applied, and the ERPG2 is the maximum concentration in the air that causes symptoms of inability to protect even if almost everyone is exposed for up to 1 hour, or irreversible or serious health effects do not appear. As the 2nd priority, 10% of the IDLH (Immediately Dangerous to Life and Health) value announced by the National Institute of Occupational Safety & Health (NIOSH) is applied, and as the 3rd priority, substances without an IDLH value can be used instead of the IDLH value in the following order. That is, 0.1 x LC50 or 0.2 x LC50 can be used, and at this time, 0.1 for a value for 30 minutes exposure and 0.2 for a value for 4 hours exposure can be applied. Alternatively, you can use 1 x LCLo, use 0.01 x LC50 (oral), or use 0.1 x LCLo (oral). At this time, the ppm of the end-point concentration is a value at 25 ℃, and when calculating the end-point distance, since the ppm value differs depending on the temperature condition of the atmosphere, it is used in consideration of this.

In addition, the safety manager terminal, safety manager terminal, and safety chief decision maker terminal receiving the alarm signal take appropriate measures for the corresponding fugitive emission facility, thereby monitoring and predicting the risk of safety accidents in advance (Step 116). Safety accidents can be prevented and safety can be improved.

In this way, the central management server 100 may predict the end point or end point concentration of the leak damage radius, and may generate big data by accumulating time-series information on the predicted end point/end point concentration. In addition, artificial neural networks and deep learning based on LSTM can be applied to the big data.

Meanwhile, the central management server 100 may transmit an alarm signal to the manager terminal 120 by performing a risk assessment on leaking air pollutants and considering the result. Here, risk assessment is a process of evaluating the degree of damage to human health or the environment when exposed to hazardous chemicals, including hazard identification, dose-response assessment, exposure analysis, and risk characterization.

The hazard identification step is a process of qualitatively identifying the harmful effects of exposure to a specific substance, the dose-response evaluation step is a process of evaluating toxicity according to the concentration of a hazardous substance, the exposure evaluation step is a process of evaluating the frequency and amount of exposure to a hazardous substance, and the risk determination step is a process of indicating the risk of exposure to a hazardous substance. Here, the risk may be quantified by a risk characterization ratio (RCR).

The RCR is divided into RCR for the human body and RCR for the environment, and the RCR for the human body uses a Derived Effect Level (DEL) and a Derived No Effect Level (DNEL). It can be expressed as in Equation 20 below.

Here, DNEL means an exposure level that does not cause adverse effects on receptors (human or environment) when exposed to hazardous substances, and is used to confirm the safety of human health of workers who may be exposed to the hazardous substances by handling hazardous substances in the workplace, consumers who may be exposed to chemicals due to the use of manufactured products, and the general public who may be indirectly exposed through the environment.

In addition, the RCR for the environment can be expressed as Equation 21 below using Predicted Effect Concentrations (PEC) and Predicted No Effect Concentrations (PNEC).

Here, PNEC means the concentration of a substance in a compartment that does not cause adverse effects on a receptor when exposed to a harmful substance for a long or short period of time. Here, AF represents a correction factor for applying the results of animal testing on the hazards of chemicals to sensitive subjects, taking into account the uncertainty and variability in the evaluation process of environmental hazards (ecological effects), and the size is determined according to the type and amount of available toxicity information. The PNEC derived by applying the AF is applied to aquatic toxicity, benthic chronic toxicity, terrestrial toxicity, and microbial activity toxicity data of sewage treatment facilities.

As another example, the PNEC can be estimated through the equilibrium distribution method, and the benthic PNECsediment and terrestrial environment PNECsoil can be estimated through the equilibrium distribution method. The PNEC estimated through the equilibrium distribution method is applied to aquatic toxicity data.

Exposure scenarios consider how the substance is manufactured or used throughout its life cycle, how the manufacturer or importer controls and recommends downstream users, how to set various conditions for exposure to humans and the environment, the uses used at each stage in the life cycle of the substance, product categories and process categories, all of which are included. In addition, exposure scenarios go through the exposure evaluation process of the environment, consumers, and workers, calculate the exposure dose according to the exposure route according to each exposure characteristic, and complete the exposure evaluation and risk determination ratio to confirm safety.

Exposure assessment consists of quantitatively or qualitatively predicting the dose/concentration of chemicals that can be exposed to the human body and the environment based on data on physical/chemical properties and hazards based on exposure scenarios for the route until contact with the receptor (human or environment) occurs. In addition, exposure evaluation is carried out by the method by the exposure evaluation model and the method by measurement, and is divided according to the stages.

Risk determination is the process of quantifying the risk with RCR and evaluating the exact nature and extent of the risk that can be caused for each exposure scenario according to the use of the chemical. In addition, these risks are managed and controlled by updating exposure assessments through appropriate risk management measures (RMMs) and elaboration of exposure scenarios.

2 is a diagram illustrating a signal flow of an air quality management system according to an embodiment of the present invention.

Referring to FIG. 2 , the illustrated atmospheric environment management system includes an IoT sensor 200, a central management server 210, and a manager terminal 220.

The IoT sensor 200 located in the fugitive emission facility periodically or time-sequentially measures air pollutants leaking from the fugitive emission facility, for example, VOCs and HAPs, and accumulates a DB based on the measurement information through a DB server connected through the IoT network. (Step 201) The IoT sensor 200 transmits the DB information to the central management server 210. (Step 203) It includes measurement information about the system, safety-related information DB, and environment-related information DB.

The central management server 210 predicts the amount of leakage of air pollutants leaked from the fugitive emission facility based on the DB information received in step 203 (step 205).

The central management server 210 compares the leak amount of the air pollutant predicted in step 205 with a predetermined threshold leak amount, and when the predicted leak exceeds the critical leak amount, the central management server 210 transmits a first alarm signal to the manager terminal 220 informing that the currently leaking air pollutant may cause environmental damage. In this case, the first alert signal is transmitted to each of a plurality of terminals. The terminals receiving the first alarm signal can prevent the emission of pollutants to the air environment and also reduce the scattering emission by taking appropriate measures to prevent environmental damage to the corresponding fugitive emission facility.

The central management server 210 predicts the end point/end point concentration of the leak damage radius based on the leak amount of the air pollutant and the area of the leak source (step 209). That is, the central management server 210 predicts the end point of the leak damage radius when an inflammable substance leaks, and predicts the end point concentration of the leak damage radius when a toxic substance leaks.

If the end point/end point concentration of the leak damage radius predicted in step 209 exceeds the critical end point/end point concentration, the central management server 210 transmits a second alarm signal to the manager terminal 220 indicating that the currently leaking material may cause a safety accident damage. (Step 211) Here, only one manager terminal 220 is shown for convenience of explanation, but the manager terminal may be a plurality of terminals related to safety management and responsibility. In this case, the second alarm signal is a plurality of terminals. are sent to each Terminals that have received the second alarm signal can prevent safety accidents and improve safety by monitoring and predicting safety accident risks in advance by taking appropriate measures to prevent safety accident damage to the corresponding fugitive emission facility.

In this way, the central management server 210 separates the environment decision-making 213 and the safety decision-making 215 to quickly and comprehensively manage complex issues related to the environment and safety of the workplace.

3 is a flowchart illustrating the central management server operation in the air quality management system according to an embodiment of the present invention.

In step 301, the central management server receives DB information from the IoT sensor located in the fugitive emission facility. The DB information includes measurement information on atmospheric pollutants leaking from the fugitive emission facility, an information DB related to safety, and an information DB related to the environment.

In step 303, the central management server predicts the leakage amount of air pollutants based on the DB information received in step 301. The amount of leakage of air pollutants can be predicted through Equations 1 to 19 described above, and detailed description thereof will be omitted here.

In step 305, the central management server checks whether the leakage amount predicted in step 303 exceeds a predetermined threshold leakage amount, and when the predicted leakage amount exceeds the critical leakage amount, proceeds to step 307 to transmit a first alarm signal informing the environment manager terminal that the currently leaking air pollutant may cause environmental damage. On the other hand, as a result of the inspection in step 305, when the predicted leakage amount is less than or equal to the critical leakage amount, the process proceeds to step 303.

In step 309, the central management server predicts the end point/end point concentration of the leak damage radius based on the amount of leakage of atmospheric pollutants and the area of the leak source. At this time, if the leaked air pollutant is an inflammable material, the central management server proceeds to step 311 and checks whether the endpoint predicted in step 309 exceeds a predetermined critical endpoint. As a result of the inspection, if the predicted end point exceeds the critical end point, the central management server proceeds to step 315 and transmits a second alarm signal notifying the safety manager terminal that the currently leaking air pollution pollutants may cause safety accident damage.

Meanwhile, if the leaked air pollutant is a toxic substance, the central management server proceeds to step 313 and checks whether the endpoint concentration predicted in step 309 exceeds a predetermined critical endpoint concentration. As a result of the inspection, if the predicted endpoint concentration exceeds the critical endpoint concentration, the central management server proceeds to step 315 and transmits the second alarm signal to the safety manager terminal.

If the endpoint/endpoint concentration predicted in both steps 311 and 313 is less than or equal to the critical endpoint/endpoint concentration, the central management server proceeds to step 309.

4 is a device diagram showing the internal configuration of a central management server related to the air quality management system according to an embodiment of the present invention.

Referring to FIG. 4, the illustrated central management server 400 includes an alert module 401, a prediction module 403, a dashboard module 405, a system module 407, a statistical analysis module 409, and a mobile support module 411.

The warning module 401 transmits an alarm signal in consideration of the value predicted by the prediction module 403, and the prediction module 403 predicts the amount of leakage of atmospheric pollutants, the end point/end point concentration of the leakage damage radius of flammable substances or toxic substances, etc., based on the accumulated DB information.

The dashboard module 405 displays the result values predicted by the prediction module 403 on the dashboard, and the mobile support module 411 also displays the result values analyzed by the statistical analysis module 409 on the manager terminal.

The system module 407 manages the alarm module 401 and the statistical analysis module 409 to operate in conjunction, and the statistical analysis module 409 accumulates the result values predicted by the prediction module 403 to generate big data for time series information, and applies artificial intelligence neural networks and deep learning based on LSTM to the big data.

Since detailed operations related to each module included in the central management server 400 are the same as those described in FIGS. 1 to 3 , detailed description thereof will be omitted here.

In the detailed description of the present invention, specific embodiments have been described, but various modifications are possible without departing from the scope of the present invention. Therefore, the scope of the present invention should not be limited to the described embodiments and should not be defined, but should be defined by not only the scope of the claims to be described later, but also those equivalent to the scope of these claims.

Claims (8)

A process of receiving database (DB: Data Base) information including measurement information, safety information, and environmental information on atmospheric pollutants from an IoT sensor installed in a fugitive emission business facility;
Based on the DB information, the critical flow pressure ratio ( _PCF /P ₁ ) is calculated based on the critical flow pressure ( _PCF ), operating pressure (P ₁ ), and specific heat coefficient (γ) in order to confirm the leakage of pollutants from the facility at the fugitive emission facility,
Leakage coefficient (C _D ), leak source area (A), the operating pressure (P ₁ ), the specific heat coefficient (γ), the gravitational constant (g _c ), molecular weight ( _MW ), gas constant (R ), based on the operating temperature (T ₁ ) Calculate the gaseous leak rate (Q ) at a leak rate of sound velocity or higher from the fugitive emission facility,
The leakage coefficient (C _D ), the leakage source area (A), the operating pressure (P ₁ ), the gravitational constant (g _c ), the molecular weight ( _MW ), the gas constant (R ), the operating temperature (T ₁ ), the specific heat coefficient (γ ), the operating pressure (P ₁ ), based on the atmospheric pressure (P _a ), calculate the gaseous leak rate (Q ) at a leak rate of less than sound speed from the facility at the fugitive emission facility,
The leakage coefficient (C _D ), the density of the hazardous material (ρ _L ), the leak source area (A), the gravitational constant (g _c ), the operating pressure (P ₁ ), the atmospheric pressure ( _Pa ), the gravitational acceleration (g ), Calculate the liquid state leakage amount (Q) from the fugitive emission business facility based on the liquid height difference (h) between the leak point and the fugitive emission business facility,
Average static pressure specific heat of a liquid ( ), the operating temperature (T ₁ ), the boiling point of the liquid under atmospheric pressure (T _b ), the average latent heat of vaporization ( ) Calculate the vapor ratio (f _v ) when the liquid-vapor state (two-phase fluid) fluid leaks from the fugitive emission facility,
The leak source area (A), the average latent heat of vaporization ( ), the vapor density at the operating pressure (ρ _G ), the density of the hazardous material (ρ _L ), the constant (K ), the gravitational constant (g _c ), the operating temperature (T ₁ ), the average static pressure specific heat of the liquid ( ) Calculate the equilibrium saturated liquid leakage (Q) from the fugitive emission plant facility based on
The leak source area (A), the average latent heat of vaporization ( ), the vapor density at the operating pressure (ρ _G ), the density of the hazardous material (ρ _L ), the constant (K), the gravitational constant (g _c ), the operating temperature (T ₁ ), the average static pressure specific heat of the liquid ( ), the length of the pipe from the outer surface of the facility to the leaking point (L _P ), and the experimental constant (L _e ) Calculate the non-equilibrium saturated liquid leakage (Q ) from the facility,
The leakage coefficient (C _D ), the density of the hazardous material (ρ _L ), the area of the leak source (A), the gravitational constant (g _c ), the operating pressure (P ₁ ), the vapor pressure at the operating temperature (P _v ), the gravitational acceleration (g ), Calculate the amount of leakage of supercooled liquid (Q ) from the fugitive emission workplace facility based on the leak point and the liquid height difference (h) in the fugitive emission workplace facility,
In order to confirm the leakage of pollutants from the pipe, the pipe friction coefficient is calculated based on the friction coefficient (f), the roughness coefficient (ε) of the pipe, and the inner diameter (D) of the pipe,
The Mach number (Ma) is calculated based on the specific heat coefficient (γ), the length of the pipe from the outer surface of the facility to the damaged part (L _P ), and the inner diameter (D) of the pipe,
Calculate the critical flow pressure ratio (P _CF /P ₁ ) based on the Mach number (Ma) and the specific heat coefficient (γ),
The specific heat coefficient (γ), the operating pressure (P ₁ ), the atmospheric pressure (P _a ), the operating temperature (T ₁ ), the length of the pipe from the outer surface of the facility for fugitive emissions to the damaged part ( _LP ), Calculate the gas leak temperature (T) based on the inner diameter (D) of the pipe,
The leak source area (A), the Mach number (Ma), the operating pressure (P ₁ ), the specific heat coefficient (γ), the gravitational constant (g _c ), the molecular weight ( _MW ), the gas constant (R ), and the operating temperature (T ₁ ) When the flow rate of the gas from the pipe is equal to or greater than the speed of sound, the gaseous leakage amount (Q) is calculated,
The leak source area (A), the gravitational constant (g _c ), the molecular weight ( _MW ), the gas constant (R), the specific heat coefficient (γ), the gas leak temperature (T ), the operating pressure (P ₁ ), The atmospheric pressure ( _Pa ), When the flow rate of the gas from the pipe is less than the sound speed based on the operating temperature (T ₁ ), Calculate the gas leak amount (Q),
Reynolds number (Re), the density of the hazardous substance (ρ _L ), the viscosity of the liquid (μL), the inner diameter of the pipe (D), the length of the pipe from the outer surface of the facility for fugitive emissions to the damaged part (L _P ), the gravitational constant (g _c ), the operating pressure (P ₁ ), the atmospheric pressure ( _Pa ), the gravitational acceleration (g), the liquid state leakage amount (Q) from the pipe based on the leak point and the liquid height difference (h) in the pipe ) is calculated,
The flow reduction coefficient (F), the leak source area (A), the average latent heat of vaporization ( ), the vapor density (ρ _G ), the density of the hazardous material (ρ _L ), the constant (K), the gravitational constant (g _c ), the operating temperature (T ₁ ), the average static pressure specific heat of the liquid ( Estimating the leakage amount of the atmospheric pollutant by calculating the aging liquid leakage amount (Q) when the liquid-vapor state fluid leaks from the pipe based on );
Transmitting a first alarm signal notifying that the leaked air pollutant may cause environmental damage to a manager terminal when the predicted leakage amount exceeds a predetermined threshold leakage amount;
transmitting a second alarm signal to the manager terminal based on an end point of a leak damage radius when the leaked air pollutant is an inflammable substance;
transmitting the second alarm signal to the manager terminal based on an end point concentration of the leak damage radius when the leaked air pollutant is a toxic substance;
Atmospheric environmental pollutant management method comprising a. According to claim 1,
When the leaked air pollutant is the inflammable material, the process of transmitting the second alarm signal to the manager terminal,
predicting the end point of the leak damage radius based on the predicted leak amount and the area of the leak source;
When the predicted end point exceeds a predetermined critical end point, transmitting the second alarm signal notifying that the leaked air pollutant may cause safety accident damage to the manager terminal;
Atmospheric environmental pollutant management method comprising a. According to claim 1,
When the leaked air pollutant is the toxic substance, the process of transmitting the second alarm signal to the manager terminal,
predicting an endpoint concentration of the leak damage radius based on the predicted leak amount and the area of the leak source;
transmitting a second alarm signal notifying that the leaked atmospheric pollutant may cause safety accident damage to the manager terminal when the predicted endpoint concentration exceeds a predetermined threshold endpoint concentration;
Atmospheric environmental pollutant management method comprising a. According to claim 1,
Accumulating information on the predicted leakage to generate big data for time series information, and applying artificial intelligence neural networks and deep learning by long short term memory (LSTM) to the big data Air pollutant management method, characterized in that.
Receive database (Data Base) information including measurement information, safety information, and environmental information on atmospheric pollutants from the IoT sensor installed in the facility of the fugitive emission workplace,
Based on the DB information, the critical flow pressure ratio ( _PCF /P ₁ ) is calculated based on the critical flow pressure ( _PCF ), operating pressure (P ₁ ), and specific heat coefficient (γ) in order to confirm the leakage of pollutants from the facility at the fugitive emission facility,
Leakage coefficient (C _D ), leak source area (A), the operating pressure (P ₁ ), the specific heat coefficient (γ), the gravitational constant (g _c ), molecular weight ( _MW ), gas constant (R ), based on the operating temperature (T ₁ ) Calculate the gaseous leak rate (Q ) at a leak rate of sound velocity or higher from the fugitive emission facility,
The leakage coefficient (C _D ), the leakage source area (A), the operating pressure (P ₁ ), the gravitational constant (g _c ), the molecular weight ( _MW ), the gas constant (R ), the operating temperature (T ₁ ), the specific heat coefficient (γ ), the operating pressure (P ₁ ), based on the atmospheric pressure (P _a ), calculate the gaseous leak rate (Q ) at a leak rate of less than sound speed from the facility at the fugitive emission facility,
The leakage coefficient (C _D ), the density of the hazardous material (ρ _L ), the leak source area (A), the gravitational constant (g _c ), the operating pressure (P ₁ ), the atmospheric pressure ( _Pa ), the gravitational acceleration (g ), Calculate the liquid state leakage amount (Q) from the fugitive emission business facility based on the liquid height difference (h) between the leak point and the fugitive emission business facility,
Average static pressure specific heat of a liquid ( ), the operating temperature (T ₁ ), the boiling point of the liquid under atmospheric pressure (T _b ), the average latent heat of vaporization ( ) Calculate the vapor ratio (f _v ) when the liquid-vapor state (two-phase fluid) fluid leaks from the fugitive emission facility,
The leak source area (A), the average latent heat of vaporization ( ), the vapor density at the operating pressure (ρ _G ), the density of the hazardous material (ρ _L ), the constant (K ), the gravitational constant (g _c ), the operating temperature (T ₁ ), the average static pressure specific heat of the liquid ( ) Calculate the equilibrium saturated liquid leakage (Q) from the fugitive emission plant facility based on
The leak source area (A), the average latent heat of vaporization ( ), the vapor density at the operating pressure (ρ _G ), the density of the hazardous material (ρ _L ), the constant (K), the gravitational constant (g _c ), the operating temperature (T ₁ ), the average static pressure specific heat of the liquid ( ), the length of the pipe from the outer surface of the facility to the leaking point (L _P ), and the experimental constant (L _e ) Calculate the non-equilibrium saturated liquid leakage (Q ) from the facility,
The leakage coefficient (C _D ), the density of the hazardous material (ρ _L ), the area of the leak source (A), the gravitational constant (g _c ), the operating pressure (P ₁ ), the vapor pressure at the operating temperature (P _v ), the gravitational acceleration (g ), Calculate the amount of leakage of supercooled liquid (Q ) from the fugitive emission workplace facility based on the leak point and the liquid height difference (h) in the fugitive emission workplace facility,
In order to confirm the leakage of pollutants from the pipe, the pipe friction coefficient is calculated based on the friction coefficient (f), the roughness coefficient (ε) of the pipe, and the inner diameter (D) of the pipe,
The Mach number (Ma) is calculated based on the specific heat coefficient (γ), the length of the pipe from the outer surface of the facility to the damaged part (L _P ), and the inner diameter (D) of the pipe,
Calculate the critical flow pressure ratio (P _CF /P ₁ ) based on the Mach number (Ma) and the specific heat coefficient (γ),
The specific heat coefficient (γ), the operating pressure (P ₁ ), the atmospheric pressure (P _a ), the operating temperature (T ₁ ), the length of the pipe from the outer surface of the facility for fugitive emissions to the damaged part ( _LP ), Calculate the gas leak temperature (T) based on the inner diameter (D) of the pipe,
The leak source area (A), the Mach number (Ma), the operating pressure (P ₁ ), the specific heat coefficient (γ), the gravitational constant (g _c ), the molecular weight ( _MW ), the gas constant (R ), and the operating temperature (T ₁ ) When the flow rate of the gas from the pipe is equal to or greater than the speed of sound, the gaseous leakage amount (Q) is calculated,
The leak source area (A), the gravitational constant (g _c ), the molecular weight ( _MW ), the gas constant (R), the specific heat coefficient (γ), the gas leak temperature (T ), the operating pressure (P ₁ ), The atmospheric pressure ( _Pa ), When the flow rate of the gas from the pipe is less than the sound speed based on the operating temperature (T ₁ ), Calculate the gas leak amount (Q),
Reynolds number (Re), the density of the hazardous substance (ρ _L ), the viscosity of the liquid (μL), the inner diameter of the pipe (D), the length of the pipe from the outer surface of the facility for fugitive emissions to the damaged part (L _P ), the gravitational constant (g _c ), the operating pressure (P ₁ ), the atmospheric pressure ( _Pa ), the gravitational acceleration (g), the liquid state leakage amount (Q) from the pipe based on the leak point and the liquid height difference (h) in the pipe ) is calculated,
The flow reduction coefficient (F), the leak source area (A), the average latent heat of vaporization ( ), the vapor density (ρ _G ), the density of the hazardous material (ρ _L ), the constant (K), the gravitational constant (g _c ), the operating temperature (T ₁ ), the average static pressure specific heat of the liquid ( ) Based on the liquid-vapor state fluid leakage from the pipe, a prediction module for predicting the leakage amount of the air pollutant by calculating the aging liquid leakage amount (Q);
When the predicted leak amount exceeds a predetermined threshold leak amount, transmits a first alarm signal indicating that the leaked air pollutant may cause environmental damage to a manager terminal, and when the leaked air pollutant is an inflammable material, transmits a second alarm signal to the manager terminal based on an endpoint of a leak damage radius, and when the leaked air pollutant is a toxic substance, transmits the second alarm signal to the manager terminal based on the endpoint concentration of the leak damage radius;
Atmospheric environmental pollutant management device comprising a. According to claim 5,
The prediction module,
When the leaked air pollutant is the inflammable substance, after estimating the endpoint of the leak damage radius based on the predicted leak amount and the area of the leak source, when the predicted endpoint exceeds a predetermined critical endpoint, the air pollution control device characterized in that for transmitting the second alarm signal informing that the leaked air pollutant may cause safety accident damage to the manager terminal. According to claim 5,
The prediction module,
When the leaked air pollutant is the toxic substance, after estimating the endpoint concentration of the leak damage radius based on the predicted leak amount and the area of the leak source, if the predicted endpoint concentration exceeds a predetermined threshold endpoint concentration, the air pollution control device characterized in that for transmitting a second alarm signal informing that the leaked air pollutant may cause safety accident damage to the manager terminal. According to claim 5,
Further comprising a statistical analysis module,
The statistical analysis module
Accumulating information on the predicted leakage to generate big data for time series information, and applying artificial intelligence neural networks and deep learning by long short term memory (LSTM) to the big data Air pollutant management device, characterized in that applied.

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