Adaptive Neuro-Fuzzy Inference System (Anfis) to Predict Ci Engine Parameters Fueled with Nano-Particles Additive to Diesel Fuel

This paper studies the use of adaptive neuro-fuzzy inference system (ANFIS) to predict the performance parameters and exhaust emissions of a diesel engine operating on nanodiesel blended fuels. In order to predict the engine parameters, the whole experimental data were randomly divided into training...

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Main Authors:	M., Ghanbari, G., Najafi, B., Ghobadian, R., Mamat, M. M., Noor, A., Moosavian
Format:	Conference or Workshop Item
Language:	English English
Published:	IOP Publishing 2015
Subjects:	TJ Mechanical engineering and machinery
Online Access:	http://umpir.ump.edu.my/id/eprint/10532/ http://umpir.ump.edu.my/id/eprint/10532/ http://umpir.ump.edu.my/id/eprint/10532/1/ADAPTIVE%20NEURO-FUZZY%20INFERENCE%20SYSTEM%20%28ANFIS%29%20TO%20PREDICT%20CI%20ENGINE%20PARAMETERS%20FUELED%20WITH%20NANO-PARTICLES%20ADDITIVE%20TO%20DIESEL%20FUEL.pdf http://umpir.ump.edu.my/id/eprint/10532/7/fkm-2015-najafi-%20Adaptive%20Neuro-Fuzzy%20Inference%20System.pdf

id	ump-10532
recordtype	eprints
spelling	ump-105322018-01-30T03:00:10Z http://umpir.ump.edu.my/id/eprint/10532/ Adaptive Neuro-Fuzzy Inference System (Anfis) to Predict Ci Engine Parameters Fueled with Nano-Particles Additive to Diesel Fuel M., Ghanbari G., Najafi B., Ghobadian R., Mamat M. M., Noor A., Moosavian TJ Mechanical engineering and machinery This paper studies the use of adaptive neuro-fuzzy inference system (ANFIS) to predict the performance parameters and exhaust emissions of a diesel engine operating on nanodiesel blended fuels. In order to predict the engine parameters, the whole experimental data were randomly divided into training and testing data. For ANFIS modelling, Gaussian curve membership function (gaussmf) and 200 training epochs (iteration) were found to be optimum choices for training process. The results demonstrate that ANFIS is capable of predicting the diesel engine performance and emissions. In the experimental step, Carbon nano tubes (CNT) (40, 80 and 120 ppm) and nano silver particles (40, 80 and 120 ppm) with nano-structure were prepared and added as additive to the diesel fuel. Six cylinders, four-stroke diesel engine was fuelled with these new blended fuels and operated at different engine speeds. Experimental test results indicated the fact that adding nano particles to diesel fuel, increased diesel engine power and torque output. For nano-diesel it was found that the brake specific fuel consumption (bsfc) was decreased compared to the net diesel fuel. The results proved that with increase of nano particles concentrations (from 40 ppm to 120 ppm) in diesel fuel, CO2 emission increased. CO emission in diesel fuel with nano-particles was lower significantly compared to pure diesel fuel. UHC emission with silver nano-diesel blended fuel decreased while with fuels that contains CNT nano particles increased. The trend of NOx emission was inverse compared to the UHC emission. With adding nano particles to the blended fuels, NOx increased compared to the net diesel fuel. The tests revealed that silver & CNT nano particles can be used as additive in diesel fuel to improve combustion of the fuel and reduce the exhaust emissions significantly. IOP Publishing 2015 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/10532/1/ADAPTIVE%20NEURO-FUZZY%20INFERENCE%20SYSTEM%20%28ANFIS%29%20TO%20PREDICT%20CI%20ENGINE%20PARAMETERS%20FUELED%20WITH%20NANO-PARTICLES%20ADDITIVE%20TO%20DIESEL%20FUEL.pdf application/pdf en cc_by http://umpir.ump.edu.my/id/eprint/10532/7/fkm-2015-najafi-%20Adaptive%20Neuro-Fuzzy%20Inference%20System.pdf M., Ghanbari and G., Najafi and B., Ghobadian and R., Mamat and M. M., Noor and A., Moosavian (2015) Adaptive Neuro-Fuzzy Inference System (Anfis) to Predict Ci Engine Parameters Fueled with Nano-Particles Additive to Diesel Fuel. In: IOP Conference Series: Materials Science and Engineering, 3rd International Conference of Mechanical Engineering Research (ICMER 2015), 18-19 August 2015 , Kuantan, Pahang. pp. 1-8., 100 (012070). ISSN 1757-8981 (Print); 1757-899X (Online) http://dx.doi.org/10.1088/1757-899X/100/1/012070
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English English
topic	TJ Mechanical engineering and machinery
spellingShingle	TJ Mechanical engineering and machinery M., Ghanbari G., Najafi B., Ghobadian R., Mamat M. M., Noor A., Moosavian Adaptive Neuro-Fuzzy Inference System (Anfis) to Predict Ci Engine Parameters Fueled with Nano-Particles Additive to Diesel Fuel
description	This paper studies the use of adaptive neuro-fuzzy inference system (ANFIS) to predict the performance parameters and exhaust emissions of a diesel engine operating on nanodiesel blended fuels. In order to predict the engine parameters, the whole experimental data were randomly divided into training and testing data. For ANFIS modelling, Gaussian curve membership function (gaussmf) and 200 training epochs (iteration) were found to be optimum choices for training process. The results demonstrate that ANFIS is capable of predicting the diesel engine performance and emissions. In the experimental step, Carbon nano tubes (CNT) (40, 80 and 120 ppm) and nano silver particles (40, 80 and 120 ppm) with nano-structure were prepared and added as additive to the diesel fuel. Six cylinders, four-stroke diesel engine was fuelled with these new blended fuels and operated at different engine speeds. Experimental test results indicated the fact that adding nano particles to diesel fuel, increased diesel engine power and torque output. For nano-diesel it was found that the brake specific fuel consumption (bsfc) was decreased compared to the net diesel fuel. The results proved that with increase of nano particles concentrations (from 40 ppm to 120 ppm) in diesel fuel, CO2 emission increased. CO emission in diesel fuel with nano-particles was lower significantly compared to pure diesel fuel. UHC emission with silver nano-diesel blended fuel decreased while with fuels that contains CNT nano particles increased. The trend of NOx emission was inverse compared to the UHC emission. With adding nano particles to the blended fuels, NOx increased compared to the net diesel fuel. The tests revealed that silver & CNT nano particles can be used as additive in diesel fuel to improve combustion of the fuel and reduce the exhaust emissions significantly.
format	Conference or Workshop Item
author	M., Ghanbari G., Najafi B., Ghobadian R., Mamat M. M., Noor A., Moosavian
author_facet	M., Ghanbari G., Najafi B., Ghobadian R., Mamat M. M., Noor A., Moosavian
author_sort	M., Ghanbari
title	Adaptive Neuro-Fuzzy Inference System (Anfis) to Predict Ci Engine Parameters Fueled with Nano-Particles Additive to Diesel Fuel
title_short	Adaptive Neuro-Fuzzy Inference System (Anfis) to Predict Ci Engine Parameters Fueled with Nano-Particles Additive to Diesel Fuel
title_full	Adaptive Neuro-Fuzzy Inference System (Anfis) to Predict Ci Engine Parameters Fueled with Nano-Particles Additive to Diesel Fuel
title_fullStr	Adaptive Neuro-Fuzzy Inference System (Anfis) to Predict Ci Engine Parameters Fueled with Nano-Particles Additive to Diesel Fuel
title_full_unstemmed	Adaptive Neuro-Fuzzy Inference System (Anfis) to Predict Ci Engine Parameters Fueled with Nano-Particles Additive to Diesel Fuel
title_sort	adaptive neuro-fuzzy inference system (anfis) to predict ci engine parameters fueled with nano-particles additive to diesel fuel
publisher	IOP Publishing
publishDate	2015
url	http://umpir.ump.edu.my/id/eprint/10532/ http://umpir.ump.edu.my/id/eprint/10532/ http://umpir.ump.edu.my/id/eprint/10532/1/ADAPTIVE%20NEURO-FUZZY%20INFERENCE%20SYSTEM%20%28ANFIS%29%20TO%20PREDICT%20CI%20ENGINE%20PARAMETERS%20FUELED%20WITH%20NANO-PARTICLES%20ADDITIVE%20TO%20DIESEL%20FUEL.pdf http://umpir.ump.edu.my/id/eprint/10532/7/fkm-2015-najafi-%20Adaptive%20Neuro-Fuzzy%20Inference%20System.pdf
first_indexed	2023-09-18T22:10:14Z
last_indexed	2023-09-18T22:10:14Z
_version_	1777414978851045376

Adaptive Neuro-Fuzzy Inference System (Anfis) to Predict Ci Engine Parameters Fueled with Nano-Particles Additive to Diesel Fuel

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