مدل‌سازی و کاهش تولید NOx در بخارساز احتراق غوطه‌ور با استفاده از سیستم استنتاج فازی

نوع مقاله : مقاله شیمی

نویسندگان

1 دانشجوی کارشناسی ارشد مهندسی‌شیمی، دانشکده شیمی و مهندسی شیمی، دانشگاه تحصیلات تکمیلی صنعتی و فناوری پیشرفته، کرمان، ایران

2 استادیار، گروه مهندسی‌شیمی، دانشکده شیمی و مهندسی شیمی، دانشگاه تحصیلات تکمیلی صنعتی و فناوری پیشرفته، کرمان، ایران

3 استادیار، گروه مهندسی شیمی، دانشکده شیمی و مهندسی شیمی، دانشگاه تحصیلات تکمیلی صنعتی و فناوری پیشرفته، کرمان، ایران

چکیده

بخارسازهای احتراق غوطه­ور از جمله تجهیزات صنعتی هستند که به میزان بسیار زیادی اکسیدهای نیتروژن (NOx) تولید می­کنند. این تجهیزات در واقع مبدل­های حرارتی هستند که در پایانه­های گاز طبیعی مایع (LNG) برای تبخیر گاز طبیعی مایع و تبدیل آن به گاز استفاده می­شوند. از آنجا که مطالعات پیشین نشان داده که شرایط عملیاتی این تجهیز بر میزان تولید NOx  در آن موثر است، در این پژوهش از ابزارهای هوش مصنوعی جهت مدل­سازی و سپس بهینه­سازی انتشار NOx در این تجهیزات استفاده شد. به همین منظور تعداد 63 داده آزمایشگاهی از پژوهش های پیشین محققان استخراج شده و سپس از ترکیبی از سیستم استنتاج فازی عصبی تطبیقی و الگوریتم ژنتیک جهت مدل­سازی داده­ها استفاده شد. در سیستم توسعه یافته، غلظت اکسیژن، دما، غلظت آب­اکسیژنه و pH محلول، به­عنوان پارامترهای ورودی به مدل و درصد کاهش NOx بعنوان خروجی در نظر گرفته شد. تحلیل های آماری مدل ساخته شده نشان داد که این مدل با ضریب همبستگی 9714/0، میانگین مربعات خطا 0938/1 ، میانگین درصد خطای مطلق 9713/4 و ماکسیمم درصد خطای مطلق 2144/13 از دقت مناسبی در تخمین مقدار کاهش NOx برخوردار است. در گام بعد و پس از توسعه مدل، از الگوریتم ژنتیک و مدل ساخته شده برای بهینه سازی شرایط عملیاتی با کمترین نرخ انتشار NOx استفاده شد. نتایج این بخش از پژوهش نیز نشان داد که در صورت بهینه سازی شرایط عملیاتی، امکان کاهش میزان NOx منتشر شده تا 24/37 درصد وجود دارد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Modeling and Reduction of Nox Production in Submerged Combustion Vaporizer Using Fuzzy Inference System

نویسندگان [English]

  • Hanieh Fani Maleki 1
  • Amir Ehsan Pheili Monfared 2
  • Mahmoud Rahmati 3
1 Master's student in Chemical Engineering, Department of Chemical Engineering, Graduate University of Advanced Technology, Kerman, Iran
2 Assistant Professor, Department of Chemical Engineering, Graduate University of Advanced Technology, Kerman, Iran
3 Assistant Professor, Department of Chemical Engineering, Graduate University of Advanced Technology, Kerman, Iran
چکیده [English]

Submerged combustion vaporizers are one of the industrial equipments that produce a large amount of nitrogen oxides (NOx). These equipments are actually heat exchangers that are used in liquefied natural gas (LNG) terminals to evaporate liquefied natural gas and convert it into gas. Since previous studies have shown that the operating conditions of this equipment are effective on the amount of NOx production in it, artificial intelligence tools were used in this research to model and then optimize NOx emission in this equipment. For this purpose, 63 laboratory data were extracted from the researchers' previous researches, and then a combination of adaptive neural fuzzy inference system and genetic algorithm was used to model the data. In the developed system, oxygen concentration, temperature, water-oxygen concentration and solution pH were considered as input parameters to the model and NOx reduction percentage as output. The statistical analysis of the built model showed that this model with correlation coefficient of 0.9714, mean square error of 1.0938, average absolute error percentage of 4.9713 and maximum absolute error percentage of 13.2144 has a good accuracy in estimating the amount of NOx reduction.  In the next step after the development of the model, the genetic algorithm and the built model were used to optimize the operating conditions with the lowest NOx emission rate. The results of this part of the research also showed that if the operating conditions are optimized, it is possible to reduce the amount of NOx released up to 37.24%

کلیدواژه‌ها [English]

  • Artificial intelligence
  • Genetic algorithm
  • Adaptive neural fuzzy inference system
  • Air pollution
  • Modeling
  • Nitrogen oxides

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مدل سازی در مهندسی
دوره 22، شماره 76
اردیبهشت 1403
صفحه 197-211

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  • تاریخ دریافت: 20 اردیبهشت 1402
  • تاریخ بازنگری: 24 تیر 1402
  • تاریخ پذیرش: 09 مهر 1402

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