مدل‌سازی اثر نانولوله‌های کربنی عامل دار حاوی اکسیژن، اضافه‌شده به مخلوط سوخت دیزل، بیودیزل و بیواتانول بر عملکرد و آلایندگی یک موتور دیزل با استفاده از شبکه عصبی مصنوعی

نوع مقاله: مقاله پژوهشی

نویسندگان

1 فارغ التحصیل مهندسی مکانیک بیوسیستم، دانشگاه علوم کشاورزیو منابع طبیعی گرگان

2 عضو هیات علمی / دانشگاه علوم کشاورزی و منابع طبیعی گرگان

3 دانشگاه علوم کشاورزی و منابع طبیعی گرگان

چکیده

چکیده: ترکیب سوخت‌های زیستی مانند بیودیزل، بیواتانول و نانولوله‌های کربنی به‌عنوان کاتالیزور به سوخت دیزل سبب عملکرد بهتر موتور و کاهش آلایندگی‌ها می‌شود. در تحقیق حاضر، برای تهیه سوخت‌های موردنیاز آزمایش، ابتدا به نسبت 5% نانولوله ­های کربنی عامل ­دار حاوی اکسیژن به سوخت دیزل اضافه شد. سپس نانولوله‏ های کربنی عامل دار با گروه اکسیژن‌دار COOH (غلظت‌های 30، 60 و ppm90) در دو سطح بیواتانول 3 و 6 درصد، با دیزل خالص و بیودیزل 5٪ ترکیب شد. آزمایش‏ ها در سه تکرار انجام شدند. در این تحقیق، مدلی با شبکه عصبی چندلایه الگوریتم یادگیری پس­ انتشار خطا روبه‌جلو (FFBP) برای تخمین عملکرد موتور ارائه شد نوع سوخت، دور موتور، چگالی، گرانروی، ارزش حرارتی سوخت، فشار چندراهِ ورودی، مصرف سوخت، دمای گازهای خروجی، دمای روغن، اکسیژن موجود در گازهای خروجی، رطوبت و فشار نسبی هوای محیط به‌عنوان پارامترهای لایه ورودی یا مستقل و عملکرد و آلایندگی موتور به‌عنوان پارامترهای لایه خروجی در نظر گرفته شدند. با توجه به نتایج به‌دست‌آمده از شبکه عصبی مصنوعی، انتشار آلایندگی‌های CO و UHC و مصرف سوخت ویژه کاهش یافت اما در انتشار NO_x شاهد افزایش بودیم. شبکه تشکیل‌شده با تابع آموزش سیگموئیدی به دلیل اینکه میزان R^2 و MSE بهتری نسبت به شبکه ‏های تشکیل‌شده خطی و تانژانت هیپربولیک، به‌عنوان مدل بهینه معرفی شد. درمجموع می‏توان بیان کرد که شبکه‏ عصبی مصنوعی توانایی مناسبی را در شبیه ‏سازی داده‏ ها و بررسی ضریب حساسیت آن‌ها نشان داده است.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Modeling the effect of adding oxygen functionalized multi-walled carbon nanotube to diesel, biodiesel, and bioethanol fuel blends on performance and emission of a diesel engine using artificial neural network

نویسندگان [English]

  • leila shakeri 1
  • ali asghari 2
  • Ahmad Taghizadeh-Alisaraei 3
1 Graduated student, Biosystems Engineering Dept., Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran
2 Assistant Professor, Biosystems Engineering Dept., Gorgan University of Agricultural Sciences and Natural Resources,
3 هیات علمی
چکیده [English]

Blended biofuels such as biodiesel and bioethanol besides adding carbon nanotubes as catalysts to diesel fuel significantly improve engine performance and reduce emissions. In this study, biodiesel (5%) was initially added to the diesel fuel to evaluate engine performance and its emissions. The studied fuels were prepared as MWENT-COOH nanoparticles (30, 60 & 90 ppm) were added to the fuels of B5 (5% biodiesel and 95% diesel) and E6 (6% bioethanol and 94% diesel) and E3 (3% bioethanol and 97% diesel). Experiments were performed in triplicates and a multilayer feed-forward back-propagation (FFBP) artificial neural network (ANN) was used for modeling the obtained results. Fuel type, engine speed, density, viscosity, the thermal value of the fuel, inlet manifold pressure, fuel consumption, exhaust gas temperature, oil temperature, oxygen in the exhaust gases, relative humidity and pressure of inlet air were considered as independent or inlet layer parameters. Output layer parameters included engine performance and emission. The results represented the decrease in emissions of CO and unburned hydrocarbons and specific fuel consumption as well as an increase in nitrogen oxides emissions. Considering the R^2 and MSE, the ANN model based on the sigmoid learning function was introduced as the optimal one in comparison to the linear and hyperbolic tangent networks. The values of the regression coefficient (R^2) were also obtained for training, evaluation, and testing of the optimal network model. In conclusion, it can be mentioned that ANN was the most effective model in simulating the obtained data and investigating the sensitivity coefficient.

کلیدواژه‌ها [English]

  • Engine Performance
  • Engine emissions
  • Carbon nanotubes
  • Artificial neural network modeling
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