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

Peyman Atarod; Esmail Khlaife; Mortaza Aghbashlo; Meisam Tabatabaei; Anh Tuan Hoang; Hossein Mobli; Mohammad Hossein Nadian; Homa Hosseinzadeh-Bandbafha; Pouya Mohammadi; Taha Roodbar Shojaei; Omid Mahian; Haiping Gu; Wanxi Peng; Su Shiung Lam

doi:10.1016/j.jhazmat.2020.124369

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

J Hazard Mater. 2021 Apr 5:407:124369. doi: 10.1016/j.jhazmat.2020.124369. Epub 2020 Oct 25.

Authors

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.

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 < 5.70 × 10^-3, RMSE < 7.55 × 10^-2, and MAPE < 3.86 × 10^-2. The optimum conditions were: injection timing of 39° CA BTDC, engine load of 74%, nanoparticle dosage of 112 μM, and water content of 2.49 wt%. The carbon dioxide, carbon monoxide, nitrogen oxides, and unburned hydrocarbon emission concentrations ‎were found to be ‎7.26‎ vol%‎, ‎0.46 vol%‎, ‎95.7‎ ppm, and‎ 36.2 ppm, respectively, under the ‎selected optimal operating conditions while the quantity of brake thermal efficiency was found at an acceptable level (‎34.0‎%).‎ In general, the applied soft computing combination appears to be a promising approach to model and optimize operating parameters and fuel composition of diesel engines.

Keywords: Adaptive neuro-fuzzy inference system; Carbon nanoparticle; Injection timing; Particle ‎swarm ‎optimization; Performance and emission characteristics; Water/diesel emulsion.