Fuel and Combustion

Fuel and Combustion

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

Document Type : Original Article

Authors
ُSeyyed Hassan Hosseini 1
Mortaza Aghbashlo 1
Meisam Tabatabaei 2
Ali Hajiahmad 1
Alireza Khataee 3
Mohammad Hossein Nadian 4
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 Microbial Biotechnology Department, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research, Extension, And Education Organization (AREEO), Karaj, Iran
3 Research Laboratory of Advanced Water and Wastewater Treatment Processes, Department of Applied Chemistry, Faculty of Chemistry, University of Tabriz, 51666-16471 Tabriz, Iran
4 Brain Engineering Research Center, Institute for Research in Fundamental Sciences
10.22034/jfnc.2022.356627.1329
Abstract
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.
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Keywords
Water/diesel emulsion
Metal-organic framework nanoparticles
Adaptive neuro-fuzzy inference system
Particle swarm optimization algorithm
Subjects
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