تعیین میزان انتشار و شبیه‌سازی پراکنش استایرن و اکریلونیتریل از واحد ABS صنعت پتروشیمی

نویسندگان
محمد شاکرخطیبی 1
رقیه یوسفی 2
خالد ظروفچی بنیس 3
سیاوش درفشی 4
1 دانشگاه علوم پزشکی تبریز
2 کمیته تحقیقات دانشجویی دانشگاه علوم پزشکی تبریز
3 دانشگاه صنعتی سهند
4 پتروشیمی تبریز
چکیده
امروزه افزایش آلودگی هوای ناشی از رشد صنعتی در کشورهای در حال توسعه به یکی از نگرانی‌های اصلی زیست‌محیطی تبدیل شده است. فرآیندهای مرتبط با نفت و گاز به‌عنوان یکی از منابع عمده آلودگی هوا محسوب می‌شوند که نشتی از تجهیزات فرایندی سهم عمده‌ای در انتشار آلاینده‌ها و به خصوص ترکیبات آلی فرار (VOCs) در این صنایع دارند. استایرن و اکریلونیتریل به‌عنوان دو ماده اصلی در فرآیند واحد ABS، امکان انتشار به محیط از نشتی‌های مختلف موجود در واحد را دارند. در مرحله اول این تحقیق، منابع اصلی انتشار این آلاینده‌ها با استفاده از اطلاعات موجود در اسناد مختلف از جمله PFD، PID و کتابچه طراحی واحد ABS شناسایی شد. سپس میزان انتشار از هر منبع با استفاده از ضرایب انتشار ارائه شده توسط سازمان حفاظت محیط زیست آمریکا (USEPA) تخمین زده شد. در نهایت، مقادیر دبی انتشار بدست آمده به‌عنوان ورودی مدل ISCST3 و به منظور بدست آوردن الگوی پراکنش این آلاینده‌ها در اطراف واحد ABS مورد استفاده قرار گرفت. نتایج نشان داد که بیشترین غلظت آلاینده استایرن در اطراف واحد µg/m3 646 می‌باشد که کمتر از غلظت مرجع ارائه شده توسط USEPA می‌باشد. بیشترین غلظت تخمین زده شده توسط مدل ISCST3 برای اکریلونیتریل نیز µg/m3 272 می‌باشد که به مراتب بیشتر از غلظت مرجع این آلاینده می‌باشد. بررسی میزان انتشار از منابع مختلف نیز نشان داد که بیشترین سهم آلاینده‌های بررسی شده به ترتیب مربوط به نشتی از کمپرسورها، پمپ‌ها و مخازن می‌باشد.

کلیدواژه‌ها

عنوان مقاله English

Estimation of Emission Rate and Simiulation of Styrene and Acrilonitryle Disperssion Around an ABS plant

نویسنده English

mohammad shakerkhatibi 1
چکیده English

Increasing pollution levels due to rapid industrialization and urbanization are now causes of major concern in industrializing countries. Petroleum and chemical processes are responsible for many emissions both into the air. Equipment leaks in chemical and petroleum processing industries are responsible for significant amount of emissions. Even if each individual leak is generally small, it is the largest source of emissions of volatile organic compounds (VOCs) from petroleum industries and chemical manufacturing facilities. Styrene and Acrylonitrile are two major components in the streams of ABS plant of Tabriz Petrochemical Complex which is expected to be released to the atmosphere through various sources such as equipment leaks and tank venting. In the first step of this study the major sources of pollutants emission in the ABS plant were identified considering the PDF and PID of the plant. Then the emission rate of each source was estimated using the emission factors presented by USEPA. An emissions factor is a representative value that attempts to relate the quantity of a pollutant released to the atmosphere with an activity associated with the release of that pollutant. Emission factors are powerful tools for policy makers as they can be used to relate emissions and concentrations. In the last step, the estimated emission rates were used as the input of Industrial Source Complex Short-Term Version 3 (ISCST3) model to predict the ground level concentration of Styrene and Acrylonitrile around the ABS plant. The ISCST3 is steady-state Gaussian plume model which can be used to assess pollutant concentrations from a wide variety of sources associated with an industrial complex. The model is generally applicable for near-field (within 10 km) impact assessment of air pollutant in meteorologically and topographically uncomplex conditions. Among the 54 pumps, 23 compressors and other equipments of the plant, 11 pumps, 8 compressors and 6 storage tanks were identified as the emission sources of considered pollutants. The emission rates of pumps and compressors were estimated using the emission factors presented in AP-42 document of USEPA. The emission estimation of Styrene and Acrylonitrile from six storage tanks has been done using USEPA standard regulatory storage tanks emission model (TANKS 4.0.9a). The emission software program TANKS is developed using emission factors presented in AP-42. The results showed that the compressors are the significant sources of considered pollutants which release about 586 g/day Styrene and 2506 g/day Acrylonitrile to the atmosphere. The emission rate of Styrene and Acrylonitrile from pumps were estimated 36 g/day and 94 g/day, respectively. The results of using TANKS model indicated that Styrene and Acrylonitrile emission rates are 7 g/day and 22 g/day, respectively. The estimated emission rates were used as the input of ISCST3 model to find the ground level concentrations of considered pollutants around ABS plant. The results showed that the maximum level of Styrene was 646 µg/m3 which is below the Reference Concentration (Rfc). In the case of Acrylonitrile the maximum level of estimated concentration was 272 µg/m3 which is higher than Rfc. The implementation of a leak detection and repair (LDAR) program or modifying/replacing leaking equipment with “leakless” components were recommended to reduce the emissions from equipment leaks of ABS plant.

کلیدواژه‌ها English

ISCST3
Styrene
Acrylonitrile
Air pollution
Simiulation
 
[1]           Xiang Y., Delbarre H., Sauvage S., Léonardis T., Fourmentin M., Augustin P., & Locoge N. 2012 Development of a methodology examining the behaviours of VOCs source apportionment with micro-meteorology analysis in an urban and industrial area. Environmental Pollution, 162, 15-28.
[2]           Leuchner M., & Rappengluck B. 2010 VOC source-receptor relationships in Houston during TexAQS-II. Atmospheric Environment, 44, 4056-4067.
[3]           Wu C. F., Wu T. G., Hashmonay R. A., Chang S. Y., Wu Y. S., Chao C. P., Hsu C. P., Chase M. J., & Kagann R. H. 2014 Measurement of fugitive volatile organic compound emissions from a petrochemical tank farm using open-path Fourier transform infrared spectrometry. Atmospheric Environment, 82, 335-342.
[4]           Yena C. H., & Horng J. J. 2009 Volatile organic compounds (VOCs) emission characteristics and control strategies for a petrochemical industrial area in middle Taiwan. Environmental Science and Health, 44(13), 1424-1429.
[5]           Cetin E., Odabasi M., &  Seyfioglu R. 2003 Ambient volatile organic compound (VOC) concentrations around a petrochemical complex and a petroleum refinery. Science of the Total Environment, 312(1-3), 103-112.
[6]           Shie R. H., & Chan C. C. 2013 Tracking hazardous air pollutants from a refinery fire by applying on-line and off-line air monitoring and back trajectory modeling. Journal of Hazardous Materials, 261, 72-82.
[7]           Ozkurt N., Sari D., Akalin N., & Hilmioglu B. 2013 Evaluation of the impact of SO2 and NO2 emissions on the ambient air-quality in the Çan–Bayramiç region of northwest Turkey during 2007-2008. Science of The Total Environment, 456-457, 254-266.
[8]           Bluett J., Gimson N., Fisher G., Heydenrych C., Freeman T., & Godfrey J. 2004 Good practice guide for atmospheric dispersion modelling. Ministry for the Environment. Wellington, New Zealand.
[9]           Abdul-Wahab S. A., Al-Alawi S. M., & El-Zawahry A. 2002 Patterns of SO2 emissions: a refinery case study. Environmental Modelling & Software, 17(6), 563-570.
[10]         Wang L., Parker D. B., Parnell C. B., Lacey R. E., & Shaw B. W. 2006 Comparison of CALPUFF and ISCST3 models for predicting downwind odor and source emission rates. Atmospheric Environment, 40(25), 4663-4669.
[11]         Chen M. H., Yuan C. S., & Wang L. C. 2015 A feasible approach to quantify fugitive VOCs from petrochemical processes by integrating open-path fourier transform infrared spectrometry measurements and industrial source complex (ISC) dispersion model. Aerosol and Air Quality Research, 15(3), 1110-1117.
[12]         Oladimeji T., Sonibare J., Odunfa M., & Ayeni A. 2015 Modeling of criteria air pollutant emissions from selected Nigeria petroleum refineries. Journal of Power and Energy Engineering, 3(6), 31-45.
[13]         Saral A., Demir S., & Yildiz S. 2009 Assessment of odorous VOCs released from a main MSW landfill site in Istanbul-Turkey via a modelling approach. Journal of Hazardous Materials, 168(1), 338-345.
[14]         Robertson E., & Barry P. J. 1989 The validity of a Gaussian plume model when applied to elevated releases at a site on the Canadian shield. Atmospheric Environment, 23(2), 351-362.
[15]         Chang C. N., Lin J. G., Chao A. C., Cho B. C., & Yu R. F. 1997 The pretreatment of acrylonitrile and styrene with the ozonation process. Water Science and Technology, 36(2-3), 263-270.
[16]         Chang C. N., Chen H. R., Huang C. H., & Chao A. 2000 Using sequencing batch biofilm reactor (SBBR) to treat ABS wastewater. Water Science and Thechnology, 41(4-5), 433-440.
 
[17]         Verner M. A., McDougall R., & Johanson G. 2012 Using population physiologically based pharmacokinetic modeling to determine optimal sampling times and to interpret biological exposure markers: The example of occupational exposure to styrene. Toxicology Letters, 213(2), 299-304.
[18]         Chang C. Y., Chang J. S., Lin Y. W., Erdei L., & Vigneswaran S. 2006 Quantification of air stripping and biodegradation of organic removal in acrylonitrile-butadiene-styrene (ABS) industry wastewater during submerged membrane bioreactor operation. Desalination, 191(1-3), 162-168.
[19]         Bahadori A. 2014 Pollution control in oil, gas and chemical plants. Springer, ISBN 978-3-319-01234-6.
[20]         Stern A. C. 1976 Air pollution: measuring, monitoring, and surveillance of air pollution. 3rd ed, Academic Press: San Diego.
[21]         Compilation of air pollutant emission factors 1995 5th ed, Office of Air Quality Planning and Standards, U. S. Environmental Protection Agency, Research Triangle Park, NC 27711.
[22]         Abdul-Wahab S. A. 2006 The role of meteorology on predicting SO2 concentrations around a refinery: A case study from Oman. Ecological Modelling, 197(1-2), 13-20.
[23] Zoroufchi Benis K., Fatehifar E., Ahmadi J., & Mohammadi M. 2015 Simulation of pollution distribution around the Tabriz oil refining
company by using ISCST model. Journal of Civil and Environmental Engineering, 44(4), 99-106.
[24]         Abdul-Wahab S. A. 2004 An application and evaluation of the CAL3QHC model for predicting carbon monoxide concentrations from motor vehicles near a roadway intersection in Muscat, Oman. Environmental Management, 34(3), 372-382.
[25]         Hassim M. H., Hurme M., Amyotte P. R., & Khan F. I. 2012 Fugitive emissions in chemical processes: The assessment and prevention based on inherent and add-on approaches. Journal of Loss Prevention in the Process Industries, 25(5), 820-829.
[26]         A review of the reference dose and concentration process 2002, Risk Assessment Forum, U.S. Environmental Protection Agency, Washington, DC 20460.
مهندسی عمران مدرس
دوره 16، شماره 5
آذر و دی 1395
صفحه 243-252