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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 (1.35 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 | ||
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| مراجع | ||
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