Soft computing-based modeling and emission control/reduction of a diesel engine fueled with carbon nanoparticle-dosed water/diesel ‎emulsion fuel

Atarod P 1 , Khlaife E 2 , Aghbashlo M 3 , Tabatabaei M 4 , Hoang AT 5 , Mobli H 6 Show all authors , Nadian MH 7 , Hosseinzadeh-Bandbafha H 8 , Mohammadi P 9 , Roodbar Shojaei T 9 , Mahian O 10 , Gu H 11 , Peng W 11 , Lam SS 12

Affiliations 

  • 1 Henan Province Engineering Research Center for Biomass Value-Added Products, School of Forestry, Henan Agricultural University, Zhengzhou 450002, China; 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 Mechatronics Engineering Department, College of Engineering, International University of Erbil, Erbil, Iraq; Biofuel Research Team (BRTeam), Terengganu, Malaysia
  • 3 Department of Mechanical Engineering of Agricultural Machinery, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran. Electronic address: maghbashlo@ut.ac.ir
  • 4 Henan Province Engineering Research Center for Biomass Value-Added Products, School of Forestry, Henan Agricultural University, Zhengzhou 450002, China; Biofuel Research Team (BRTeam), Terengganu, Malaysia; Institute of Tropical Aquaculture and Fisheries (AKUATROP), Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu, Malaysia; Microbial Biotechnology Department, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran
  • 5 Ho Chi Minh City University of Technology (HUTECH), Ho Chi Minh City, Vietnam
  • 6 Department of Mechanical Engineering of Agricultural Machinery, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
  • 7 Brain Engineering Research Center, Institute for Research in Fundamental Sciences (IPM), P.O. Box 19395-5531, Tehran, Iran
  • 8 Department of Mechanical Engineering of Agricultural Machinery, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran; Biofuel Research Team (BRTeam), Terengganu, Malaysia
  • 9 Biofuel Research Team (BRTeam), Terengganu, Malaysia
  • 10 School of Chemical Engineering and Technology, Xi'an Jiaotong University, Xi'an 710049, China; Renewable Energy and Micro/Nano Sciences Lab., Department of Mechanical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
  • 11 Henan Province Engineering Research Center for Biomass Value-Added Products, School of Forestry, Henan Agricultural University, Zhengzhou 450002, China
  • 12 Henan Province Engineering Research Center for Biomass Value-Added Products, School of Forestry, Henan Agricultural University, Zhengzhou 450002, China; Institute of Tropical Aquaculture and Fisheries (AKUATROP), Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu, Malaysia. Electronic address: lam@umt.edu.my
J Hazard Mater, 2021 04 05;407:124369.
PMID: 33160782 DOI: 10.1016/j.jhazmat.2020.124369

Abstract

This study was set up to model and optimize the performance and emission characteristics of a diesel engine fueled with carbon nanoparticle-dosed water/‎diesel emulsion fuel using a combination of soft computing techniques. Adaptive neuro-fuzzy inference system tuned by particle ‎swarm algorithm was used for modeling the performance and emission parameters of the engine, while optimization of the engine operating parameters and the fuel composition was conducted via multiple-objective particle ‎swarm algorithm. The model input variables were: injection timing (35-41° CA BTDC), engine load (0-100%), nanoparticle dosage (0-150 μM), and water content (0-3 wt%). The model output variables included: brake specific fuel consumption, brake thermal efficiency, as well as carbon monoxide, carbon dioxide, nitrogen oxides, and unburned hydrocarbons emission concentrations. The training and testing of the modeling system were performed on the basis of 60 data patterns obtained from the experimental trials. The effects of input variables on the performance and emission characteristics of the engine were thoroughly analyzed and comprehensively discussed as well. According to the experimental results, injection timing and engine load could significantly affect all the investigated performance and emission parameters. Water and nanoparticle addition to diesel could markedly affect some performance and emission parameters. The modeling system could predict the output parameters with an R2 > 0.93, MSE 

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.

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