Mediterranean Conference on Embedded Computing ,,/"MECO - 2012 Bar, Montenegro

Hardware-software system for simulation of hazardous gas releases

Vladimir Popovic, Radovan Stojanovic, Marko Dragovic and Jovan Kovacevic University of Montenegro, Faculty of Electrical Engineering, Dzordza Vasintona bb.,

81000 Podgorica, Montenegro vlad.popovic@hotmail.com, dragovic@t-com.me, jovan.k@live.co.uk

Andrej Skraba University of Maribor, Faculty of Organizational Sciences,

Kranj, Slovenia

Simon Berkowicz Hebrew University of Jerusalem, Arid Ecosystems Research

Center, Jerusalem, Israel

Abstract: In this paper we describe GEPSUS-W, a wearable

system for hazardous gases dispersion simulation in case of man­

made accidents. The system architecture, simulation

methodology and preliminary results are elaborated. Emphasis

is given to real-time embedded systems that allow automatic

data entry and on the spot responder interaction with situations.

The approach and system are verified in real conditions.

Keywords: hazardous gas, simulation, dispersion, GIS, embedded system

I. INTRODUCTION

When hazardous gases are released into the atmosphere, whether accidentally or due to a terrorist attack, emergency response authorities require quick and relevant information about affected populations and infrastructure. The process is time-critical, especially in urban areas, because of population density and consequences of a delayed response ..

Hence, there is a pressing need from emergency responders and civil protection stakeholders to benefit from information and communication technologies (lCT).

Existing air pollutant modeling software are off-line and predominantly model pollutant dispersion in 2D and 3D space displaying the concentration profiles (plumes) over digital maps [1]. The plumes are static and do not consider the dynamics of the process, primarily the changes in atmospheric conditions and source strength. They do not support automatic data importing, incorporation of weather forecasts and, most importantly, decision-making which is a fundamental issue for

This research is supported by the NATO Programme Science for Peace (Sfp) under grant SFP 9835 10.

Raffaele de Amicis Fondazione GraphiTech, Via alia Cascata 56/C,

38 I 23 Trento, Italy

Mira Cerovic Ministry of Defense Of Montenegro,

Montenegro

fast and accurate response. Additionally, approaching the situation from the field, as an example using simple Personal Digital Assistance (PDA) devices, should be an essential component.

On the other hand, a useful system for management and control of accidental/deliberate releases of hazardous gases should at least be real-time with the possibility to integrate several subsystems; a) Geographical Information System (GIS), b) system for measurement and monitoring of chemical parameters, c) system for hydro meteorological monitoring and forecasts, d) system for modeling gas dispersion, e) local sensor networks, and e) system for planning emergency responses [2].

This paper describes GEPSUS-W, a Wearable System for Simulation of Hazardous Gases Dispersion in case of accidents caused by industrial, storage and transport facilities, like factories, static and vehicle tanks and pipes. GEPSUS-W calculates threat zones and visualizes them over the GIS data in real time. At the same it considers on-the-spot weather conditions such as as wind speed and direction as well as source parameters and terrain characteristics. Additionally, the GEPSUS-W can be used in training first responders and other stakeholders involved in emergency management.

Below we describe the system architecture and modeling and visualization methodology with emphasis on embedded systems for both automatic data collecting and importing and on the spot responder interfacing where the latest Personal Digital Assistance (PDA) technology is employed.

Mediterranean Conference on Embedded Computing ,,/"MECO - 2012 Bar, Montenegro

[I. METHODOLOGY

The architecture of the GEPSUS-W system is shown in Fig. 1. Roughly, it consists of three units: LOCAL [NTERF AC[NG/EMULAT[ON, MODELING AND VISUALISATION and RESPONDER INTERACTION. It is an integral part of the global GEPSUS system, which still includes GLOBAL INTERFACING AND DECISION MAK[NG.

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Figure 1: GEPSlIS-W architecture as a part of global GEPSlIS system

A. LOCAL INTERFACING/EMULATION

The function of LOCAL INTERFACINGIEMULATION is to provide input data for the modeling and simulation unit (Fig. 2). [n fact, it is a system located near to critical objects consisting of local sensors or weather/chemo stations and devices for their collection and forwarding. The local sensors or weather/stations observe on the spot conditions in terms of pollutant concentration and weather parameters. COLECTORIEMULATOR manages the sensor data and can work in two modes.

In COLLECTOR mode, capturing input data from external sensors or surrounding stations or facility/factory measurement systems, performing their preprocessing and forwarding them to the modeling host. [n EMULATOR mode it generates a sequence of pre-recorded data or typical scenarios for the purpose of exercises. Designing of pre-recorded data and scenarios are done by emergency experts. In both modes the outputs from COLECTOR/EMULA TOR are inputs which describe pollutant emission rate, the type and geometry of the source, atmospheric stability classes, temperature, wind speed and direction etc.

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[n this research, as a local weather station we used a La Crosse TX20 Wind Anemometer with Direction (Fig. 2). It has a wind direction encoder disk that can handle 16 different positions. Disk is encoded in Gray code. An on board micro controller converts Gray code into 4 bits binary code. Wind speed has 12 bits value (3 LSB's are always 0) with a maximal value of 51.1 mls and resolution of 0.1 mls. Every two seconds TX20 transmits a 41 bits long sequence to COLLECTORIEMULATOR. Data sequence consists of six data segments: A - start frame (5 bits), B - inverted wind direction (4 bits), C - inverted wind speed (12 bits), D -Checksum (4 bits), E - wind direction (4 bits), F - wind speed (12 bits). La Crosse TX20 Anemometer datagram is shown in Fig. 3.

A c -

1

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Figure 3: La Crosse TX20 Anemometer datagram

Functionally seen, the COLLECTORIEMULA TOR unit consists of a micro controller, input data interface, output data interface, display and user interface. Input interface can collect analog data (0 - 5V, 4 - 20mA) via the ADC of the microcontroller and digital data via an appropriate protocol

Mediterranean Conference on Embedded Computing ,,/"MECO - 2012

(RS232, I-Wire, SPI. . . ). Data can be received and sent by cable or wirelessly (RF, B1uetooth, ZigBee). The function of the micro controller is to collect data from sensors, analyze and convert them to appropriate output format. The microcontroller's EEPROM memory is used for storage data of typical scenarios. Using a user interface, data can be modified, saved, loaded from memory and sent to the PC host.

B. Modeling and visualization

The MODELING and V[SUALlZAT[ON unit is located on a remote host/server. It performs dispersion modeling and calculation over space and time, considering input parameters entered automatically or manually. The output from this unit is a set of threat zones.

Within the GEPSUS project, for modeling purposes we adopted a Gaussian dispersion model for following sources: factory stacks, storage or transportation tanks and pipelines [3]. The concentration of the pollutant is calculated as a 3D matrix C(x,y,z) in ug/m3, where x presents downwind direction, y crosswind and z altitude. For z=const, C(x,y,z) becomes a 2D matrix. At ground level z=o it corresponds to C(x,y, 0), Fig. 4. The threat zones are obtained from C(x,y,z) as contour plots Col (x,y) ... Con(x,y), for pre-defmed Levels of Concern (LOCs), where C(x,y,O)=LOCI, . . . C(x,y,O)=LOCn respectively. LOCs, often called thresholds, are associated for each gas and standardized by different guidelines as Emergency Response Planning Guidelines (ERPGs) or Acute Exposure Guideline Levels (AEGLs).

In order to be visualized over GIS data, the threat zones should be exported in some format readable by standard Geo Browsers. We used a "KML" file format compatible with Google Earth. [t is an open standard officially named the OpenGIS KML Encoding Standard (OGC KML) [4]. Before generating KML, the contour graphs given in meters should be transferred into latitude-longitude coordinates taking in account a source position and wind angle.

The operator communicates with modeling software via GU[ (Fig. 5), which receives input data manually or automatically. The modeling and visualization application should work as a server or stand alone application.

Generated "KML" files are automatically uploaded to the GEPSUS web server. Responders or staff of the Emergency Center activate this file and visualize its content through a GEPSUS-WEB interface

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VISUALIZATION unit

Mediterranean Conference on Embedded Computing ,,/"MECO - 2012 Bar, Montenegro

GEPSUS MOO V.COl

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Figure 5: GEPSUS-W GUt for importing parameters

C. Responder interaction The responder communicates with modeling outputs

through a GEPSUS-WEB interface, which is in fact a web application written in PHP and JavaScript. The function of the GEPSUS-WEB interface is to visualize calculated threat zones (in the form of KML files) over Google Earth - Maps Engine and to allow responder interaction, such as entering data rrom field.

The GEPSUS-WEB can visualize single source or multi­source data. As web application, adjusted for low data transfers, even GPRS can be accessed by any Web browser installed on any machine (desktop, PDA, mobile phone, etc). GUI is simple and easy to use and is of importance in stressful situations. Emergency responders at every moment can get real-time visualization of threat zones. Figure 6 shows responder interaction through a smart phone. The plume shape and direction are real-time functions of weather conditions and source dynamics.

Figure 6: Threat zones presented on a smart phone

III. RESULTS

We verified the developed approach within GEPSUS-W by simulation of hypothetical accidents on existing critical objects in Montenegro such as the Thermo Power Plant Pljevlja. The following data are entered manually:

Source location (lat,lon)=43 .334269, 19.327522, Gas=S02; Emission rate, Q[g/s]=918; Actual stack height, hs [m]=250; Stack diameter, Ro [m]=7.5m, Ambient temp., T (K)=286; Gas temp on exit T(K)=413; Speed of pollutant on exit (m/s)= 6.3, Pasquill stability class="B"; Terrain="urban", Retlection="rrom ground", Critical LOC I [J.tg/m3]= 25; LOC2=50, LOC3=75.

Wind speed and wind direction are imported automatically from the La Crosse TX20 sensor via COLLECTOR/EMULATOR (Fig. 2). Every 2 seconds, the sensor sends information about wind speed and wind direction. The spread of the plume at different moments (TI=9h and T2=llh) are illustrated in Fig. 7 and 8. As seen, the wind changed speed and direction, resulting in different plume size and orientation. The situation was handled by a smart phone.

Figure 7: The plume positiou at

09:00, D=21oo, v=2 m/s

Figure 8: The plume positions at 11 :00, D=24oo, v=5 m/s

IV. CONCLUSION

In this paper is presented system for simulation of hazardous gas release in case of manmade accidents. System architecture, modeling methodology and visualization is described. Emphasis is given on embedded system for automatic data collecting and importing and on the spot responder interaction. Result of one typical scenario is shown. This system can be used in real time emergency situation or for training, education and risk assessment.

REFERENCES

[1] Baumann-Stanzer, K. and Stenzel, S. (2011) Uncertainties in modeling hazardous gas releases for emergency response. Meteorologische Zeitschrift. Volume 20(1),19-27.

[2] Stojanovi6, R., Skraba, A., de Amicis, R., Conti, G., Elhanani, D. & Berkowicz, S, "Integration of System Simulation and Geographical Information Processing for the Air-Pollution Emergency Situations Control and Decision Making", InterSymp 2011, Baden Baden, Germany.

[3] Beychok, M. R., "Fundamentals of Stack Gas Dispersion", 4th Edition ed., 2005, ISBN 0-9644588-0

[4] OpenG L. (2012) htlp://www.opengeospatial.org/standards/kml, Accessed, 12.1.2012