پیوندهای مفید
آمار
| تعداد نشریات | 8 |
| تعداد شمارهها | 443 |
| تعداد مقالات | 5,691 |
| تعداد مشاهده مقاله | 7,862,076 |
| تعداد دریافت فایل اصل مقاله | 6,421,681 |
شبیه سازی ریاضی و شبکه عصبی مصنوعی کاهش کاتالیستی انتخابی ناکس در یک راکتور مونولیتی | ||
| نشریه مهندسی مکانیک امیرکبیر | ||
| مقاله 7، دوره 52، شماره 9، آذر 1399، صفحه 2465-2478 اصل مقاله (1.73 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22060/mej.2019.15355.6102 | ||
| نویسندگان | ||
| علی فرضی* ؛ پروانه خلعتی | ||
| دانشکده مهندسی شیمی و نفت، دانشگاه تبریز، تبریز، ایران | ||
| چکیده | ||
| گسترش صنایع و افزایش مصرف انرژی در جهان، سبب افزایش انتشار آلاینده اکسیدهای نیتروژن، ناکس، شده است. بنابراین حذف ناکس از اهمیت بسیاری برخوردار است. در این مطالعه، مدلسازی و شبیهسازی کاهش کاتالیستی انتخابی ناکس توسط آمونیاک در یک راکتور کاتالیستی مونولیتی در دو حالت پایا و دینامیک انجام گردید. نتایج حالت پایا نشان داد که به دلیل اثر شدید دما بر تبدیل ناکس و رقابت واکنش اصلی با اکسیداسیون آمونیاک، تبدیل ناکس نیاز به فیلتر کاتالیستی در محدوده 300 تا 350 درجه سانتیگراد دارد. نتایج نشان داد که تبدیل اکسید نیتروژن با کاهش سرعت فضایی گاز و افزایش غلظت اکسید نیتروژن ورودی افزایش مییابد. در حالت دینامیک اثر تغییرات پارامترهای مؤثر شامل سرعت فضایی گاز، غلظت اکسید نیتروژن و نسبت مولی آمونیاک به اکسید نیتروژن ورودی مورد بررسی قرار گرفت. همچنین شبیهسازی حالت پایای فرایند با شبکه عصبی مصنوعی انجام گرفت و مقادیر تبدیل اکسید نیتروژن و آمونیاک به عنوان تابعی از سرعت فضایی گاز، دمای راکتور و غلظت اکسید نیتروژن تخمین زده شدند. 96 شبکه با تعداد نرونهای مختلف و دو تابع فعا لسازی مختلف در لایه مخفی آموزش داده شدند. شبکه بهینه بیشینه خطای مربعی حدود 01 / 0 نسبت به نتایج مدلسازی ریاضی نشان داد که حاکی از کارآیی بالای شبکه عصبی در پیشبینی عملکرد فرایند میباشد. | ||
| کلیدواژهها | ||
| کاهش کاتالیستی انتخابی ناکس؛ راکتور مونولیتی لانه زنبوری؛ مدل سازی ریاضی؛ شبیه سازی فرایند؛ شبکه عصبی مصنوعی | ||
| عنوان مقاله [English] | ||
| Mathematical and Artificial Neural Network Simulation of NOx Selective Catalytic Reduction in a Monolithic Reactor | ||
| نویسندگان [English] | ||
| Ali Farzi؛ Parvaneh Khalati | ||
| Faculty of Chemical and Petroleum Engineering, University of Tabriz, Tabriz, Iran | ||
| چکیده [English] | ||
| Worldwide development of industries and increase of energy consumption, have resulted fast increase in the emission of nitrogen oxides pollutants. Therefore, removal of nitrogen oxides is a very important issue. In this study, modeling and simulation of selective catalytic reduction of nitrogen oxides in a monolithic catalytic reactor at both steady-state and dynamic-state was performed. Steady- state results showed that because of intense effect of temperature on nitrogen oxides conversion and competition of the main reaction with ammonia oxidation reaction, conversion of nitrogen oxides requires a catalytic filter in the range 300-350°C. Results showed that nitrogen oxide conversion increases with decreasing gas hourly space velocity and increasing inlet nitrogen oxide concentration. At dynamic-state, the effect of changes in some parameters including gas hourly space velocity, inlet nitrogen oxide concentration, and ammonia /nitrogen oxide ratio were investigated. Also, steady-state simulation of the process was performed using an artificial neural network and conversions of nitrogen oxides and ammonia were estimated as a function of gas hourly space velocity, reactor temperature, and nitrogen oxide concentration. 96 networks with different neurons and two different activation functions in hidden layer were trained. The resulted optimum network showed maximum mean square error of about 0.01 compared to mathematical modeling results indicating high performance of neural network for prediction of process performance. | ||
| کلیدواژهها [English] | ||
| Selective catalytic reduction of NOx, Honey-comb monolithic reactor, Mathematical modeling, Process simulation, Artificial neural network | ||
| مراجع | ||
|
[1] G. Schaub, D. Unruh, J. Wang, T. Turek, Kinetic analysis of selective catalytic NOx reduction (SCR) in a catalytic filter, Chemical Engineering and Processing: Process Intensification, 42(5) (2003) 365-371. [2] B. Guan, R. Zhan, H. Lin, Z. Huang, Review of state of the art technologies of selective catalytic reduction of NOx from diesel engine exhaust, Applied Thermal Engineering, 66(1) (2014) 395-414. [3] G. Saracco, V. Specchia, Simultaneous removal of nitrogen oxides and fly-ash from coal-based power-plant flue gases, Applied Thermal Engineering, 18(11) (1998) 1025-1035. [4] S.R. Ness, G.E. Dunham, G.F. Weber, D.K. Ludlow, SCR catalyst-coated fabric filters for simultaneous NOx and high-temperature particulate control, Environmental Progress, 14(1) (1995) 69-74. [5] G. Saracco, V. Specchia, Catalytic filters for the abatement of volatile organic compounds, Chemical Engineering Science, 55(5) (2000) 897-908. [6] C. Winkler, P. Flörchinger, M.D. Patil, J. Gieshoff, P. Spurk, M. Pfeifer, Modeling of SCR DeNOx Catalyst - Looking at the Impact of Substrate Attributes, SAE Transactions, 112 (2003) 691-699. [7] E. Tronconi, Interaction between chemical kinetics and transport phenomena in monolithic catalysts, Catalysis Today, 34(3) (1997) 421- 427. [8] Z. Lei, X. Liu, M. Jia, Modeling of selective catalytic reduction (SCR) for NO removal using monolithic honeycomb catalyst, Energy & Fuels, 23(12) (2009) 6146-6151. [9] H. Xua, F. Tub, Z. Hec, J. Mad, Q. Wange, Modelling of the selective catalytic NOx reduction for diesel engine, Applied Mechanics and Materials, 71-78(2098-2102) (2011) 2098. [10] L. Sharifian, Y.M. Wright, K. Boulouchos, M. Elsener, O. Kröcher, Calibration of a model for selective catalytic reduction with ammonia, including NO oxidation, and simulation of NOx reduction over an Fe–zeolite catalyst under highly transient conditions, International Journal of Engine Research, 14(2) (2012) 107- 121. [11] B.K. Yun, M.Y. Kim, Modeling the selective catalytic reduction of NOx by ammonia over a Vanadia-based catalyst from heavy duty diesel exhaust gases, Applied Thermal Engineering, 50(1) (2013) 152-158. [12] M. Aghbashlo, S. Hosseinpour, A.S. Mujumdar, Application of artificial neural networks (ANNs) in drying technology: A comprehensive review, Drying Technology, 33(12) (2015) 1397-1462. [13] M. Mohanraj, S. Jayaraj, C. Muraleedharan, Applications of artificial neural networks for thermal analysis of heat exchangers – A review, International Journal of Thermal Sciences, 90 (2015) 150-172. [14] H. Li, Z. Zhang, Z. Liu, Application of artificial neural networks for catalysis: A review, Catalysis, 7(10) (2017) 306-324. [15] W.G. Baxt, Application of artificial neural networks to clinical medicine, Lancet, 346 (1995) 1135-1138. [16] G. Jinescu, V. lavric, The artificial neural networks and the drying process modeling, Drying Technology, 13(5-7) (1995) 1579-1586. [17] E. Assidjo, B. Yao, K. Kisselmina, D. Amané, Modeling of an industrial drying process by artificial neural networks, Brazilian Journal of Chemical Engineering, 25 (2008) 515-522. [18] C. Oliveira, P. Georgieva, F. Rocha, S. Feyo de Azevedo, Artificial neural networks for modeling in reaction process systems, Neural Computing and Applications, 18(1) (2009) 15- 24. [19] S. Nandi, P. Mukherjee, S.S. Tambe, R. Kumar, B.D. Kulkarni, Reaction Modeling and Optimization Using Neural Networks and Genetic Algorithms: Case Study Involving TS-1-Catalyzed Hydroxylation of Benzene, Industrial & Engineering Chemistry Research, 41(9) (2002) 2159-2169. [20] A.L.N. Mota, O. Chiavone-Filho, S.S. da SilvaSyllos, E.L. Foletto, J.E.F. Moraes, C.A.O.Nascimento, Application of artificial neural network for modeling of phenol mineralization by photo-Fenton process using a multi-lamp reactor, Water Science & Technology, 69(4) (2014) 768-774. [21] F. Calivá, F.S. De Ribeiro, A. Mylonakis, C. Demazi’ere, P. Vinai, G. Leontidis, S. Kollias, A Deep Learning Approach to Anomaly Detection in Nuclear Reactors, in: 2018 International Joint Conference on Neural Networks (IJCNN), IEEE, Rio de Janeiro, Brazil, 2018. [22] M.-S. Izadkhah, A. Farzi, Mathematical and artificial neural network modeling of production of ethylene from ethane pyrolysis in a tubular reactor, Petroleum Science and Technology, 36(11) (2018) 732-738. [23] C.W. Baxter, S.J. Stanley, Q. Zhang, D.W. Smith, Developing artificial neural network models of water treatment processes: a guide for utilities, Journal of Environmental Engineering and Science, 1 (2002) 201-211. [24] S.S. Madaeni, G. Zahedi, M. Aminnejad, Artificial neural network modeling of O2 separation from air in a hollow fiber membrane module, Asia-Pacific Journal of Chemical Engineering, 3(4) (2008) 357-363. [25] K.C. Lai, S.K. Lim, P.C. Teh, K.H. Yeap, Modeling Electrostatic Separation Process Using Artificial Neural Network (ANN), Procedia Computer Science, 91 (2016) 372- 381. [26] J. Mohammadhassani, S. Khalilarya, M. Solimanpur, A. Dadvand, Prediction of NOx emissions from a direct injection diesel engine using artificial neural network, Modelling and Simulation in Engineering, 2012 (2012) 1-8. [27] M. Fischer, Transient NOx estimation using artificial neural networks, IFAC Proceedings Volumes, 46(21) (2013) 101-106. [28] J.D. Martínez-Morales, E.R. Palacios-Hernández, G.A. Velázquez-Carrillo, Modeling engine fuel consumption and NOx with RBF neural network and MOPSO algorithm, International Journal of Automotive Technology, 16(6) (2015) 1041-1049. [29] E. Majd Faghihi, A.H. Shamekhi, Development of a neural network model for selective catalytic reduction (SCR) catalytic converter and ammonia dosing optimization using multi objective genetic algorithm, Chemical Engineering Journal, 165 (2010) 508-516. [30] R. Serra, M.J. Vecchietti, E. Miró, A. Boix, In,Fe-zeolites: Active and stable catalysts for the SCR of NOx—Kinetics, characterization and deactivation studies, Catalysis Today, 133- 135 (2008) 480-486. [31] W.E. Schiesser, The numerical method of lines, Academic Press, San Diego, CA, 1991. [32] M. Aliramezani, C.R. Koch, R.E. Hayes, Estimating tailpipe NOx concentration using a dynamic NOx/ammonia cross sensitivity model coupled to a three state control oriented SCR model, IFAC-PapersOnLine, 49(11) (2016) 8-13. [33] B. Krose, P. van der Smagt, An introduction to neural networks, 8 ed., The University of Amsterdam, Amsterdam, Netherlands, 1996. [34] S.A. Hejazi, K.R. Jackson, Efficient valuation of SCR via a neural network approach, Journal of Computational and Applied Mathematics, 313 (2017) 427-439. [35] K. Hubner, A. Pape, E.A. Weber, Simultaneous removal of gaseous and particulate components from gases by catalytically activated ceramic filters, in: High Temperature Gas Cleaning, 1996, pp. 267-277. | ||
|
آمار تعداد مشاهده مقاله: 897 تعداد دریافت فایل اصل مقاله: 1,012 |
||