مدل‌سازی و بهینه سازی عملکرد و آلایندگی یک موتور دیزلی سوخت رسانی شده با امولسیون های آب- دیزل حاوی افزودنی نانوذرات فلزی-آلی به کمک یادگیری ماشین

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی مکانیک ماشین‌های کشاورزی، دانشکده فنی و مهندسی کشاورزی، دانشکده کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران

2 گروه مهندسی ماشینهای کشاورزی، دانشکده مهندسی و فناوری کشاورزی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران

3 گروه بیوتکنولوژی میکروبی ، پژوهشکده بیوتکنولوژی کشاورزی ایران ، سازمان تحقیقات کشاورزی، ترویج و آموزش، کرج، ایران

4 آزمایشگاه تحقیقاتی فرآیندهای پیشرفته تصفیه آب و فاضلاب ، گروه شیمی کاربردی ، دانشکده شیمی ، دانشگاه تبریز ، تبریز ، ایران

5 پژوهشکده علوم شناختی، پژوهشگاه دانش‌های بنیادی، تهران، ایران

10.22034/jfnc.2022.356627.1329

چکیده

تحقیق حاضر، به‌منظور مدل سازی و بهینه سازی عملکرد و ویژگی های انتشار آلایندگی یک موتور دیزل سوخت رسانی شده با امولسیون آب-دیزل حاوی نانوذرات چارچوب فلزی-آلی با استفاده از ترکیب سامانه استنتاج عصبی-فازی تطبیقی با الگوریتم بهینه سازی ازدحام ذرات (PSO-ANFIS) انجام شده است. به منظور بهینه سازی پارامترهای عملکرد موتور و ترکیب سوخت از الگوریتم ازدحام ذرات چند منظوره (MOPSO) استفاده شده است. مقدار آب اضافه شده به امولسیون، بار موتور و غلظت افزودنی نانوذرات چارچوب فلزی-آلی به عنوان پارامترهای ورودی مدل در نظر گرفته شدند. مصرف سوخت ویژه ترمزی، بازده حرارتی ترمزی، CO، CO2، UHC، NOx و دوده به عنوان خروجی مدل در نظر گرفته شده اند. از 16 داده تجربی در فرآیند مدل‌سازی و بهینه سازی استفاده شده است. نتایج نشان داد مدل های توسعه یافته PSO-ANFIS با دقت کافی توابع هدف را پیش بینی می‌کند. بین تمامی داده‌های هدف و خروجی مدل های توسعه یافته تطابق خوبی وجود داشت. با توجه به نتایج بهینه‌سازی مشاهده شد که سوخت امولسیون آب-دیزل حاوی 27/26 ppm نانوذره چارچوب فلزی-آلی و 14/4 درصد وزنی آب تحت بار موتور 15/60 درصد از بار کامل دارای شرایط بهینه می‌باشد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Modeling and optimization of performance and emissions of a diesel engine fueled with water-diesel emulsions containing metal-organic nanoparticles by machine learning

نویسندگان [English]

  • ُSeyyed Hassan Hosseini 1
  • Mortaza Aghbashlo 2
  • Meisam Tabatabaei 3
  • Ali Hajiahmad 2
  • Alireza Khataee 4
  • Mohammad Hossein Nadian 5
1 Department of Mechanical Engineering of Agricultural Machinery, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
2 Department of Mechanical Engineering of Agricultural Machinery, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
3 Microbial Biotechnology Department, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research, Extension, And Education Organization (AREEO), Karaj, Iran
4 Research Laboratory of Advanced Water and Wastewater Treatment Processes, Department of Applied Chemistry, Faculty of Chemistry, University of Tabriz, 51666-16471 Tabriz, Iran
5 Brain Engineering Research Center, Institute for Research in Fundamental Sciences
چکیده [English]

The present study aimed to model and optimize the performance and emission characteristics of a diesel engine fueled with water-diesel emulsions containing metal-organic framework nanoparticles using a combination of adaptive neural-fuzzy inference system with optimal algorithm particle swarm generation (PSO-ANFIS). The multi-purpose particle swarm algorithm (MOPSO) was used to optimize engine performance and fuel composition. Water inclusion rate, engine load, and metal-organic framework nanoparticle concentration were considered as input parameters of the model. Brake specific fuel consumption, brake thermal efficiency, CO, CO2, UHC, NOx, and smoke were considered as model outputs. Sixteen experimental data were used in modeling and optimization processes. The results showed that the developed PSO-ANFIS models could accurately predict the objective functions. There was a good agreement between all the target data and the output of the developed models. According to the optimization results, water-diesel emulsion fuel containing 26.27 ppm metal-organic framework nanoparticles and 4.14 wt% water under engine load 60.15% of the full-load operating level was found to be optimal conditions.
.

کلیدواژه‌ها [English]

  • Water/diesel emulsion
  • Metal-organic framework nanoparticles
  • Adaptive neuro-fuzzy inference system
  • Particle swarm optimization algorithm

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  • تاریخ دریافت: 26 مرداد 1401
  • تاریخ بازنگری: 26 مهر 1401
  • تاریخ پذیرش: 29 مهر 1401
  • تاریخ اولین انتشار: 01 آبان 1401

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