Substitution of diesel fuel in conventional compression ignition engine with waste biomass-based fuel and its validation using artificial neural networks
This study aims to derive bioenergy from waste lather fat and citronella grass. Lather fat oil (LFO), citronella grass oil (CGO), a mixture of leather fat oil and citronella grass oil (LFCGO), and a nano-additive-incorporated mixture of lather fat oil and citronella grass oil (NFCO) were synthesized...
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my.uniten.dspace-341222024-10-14T11:18:03Z Substitution of diesel fuel in conventional compression ignition engine with waste biomass-based fuel and its validation using artificial neural networks Ramalingam K. Venkatesan E.P. Vellaiyan S. Mukhtar A. Sharifpur M. Yasir A.S.H.M. Saleel C.A. 57307229100 57221721602 56695172500 57195426549 23092177300 58518504200 57197875592 Lather fat oil Nano additive NOx emission Peel oil Waste to energy ASTM standards Biodiesel Biomass Brakes Carbon dioxide Carbon monoxide Diesel engines Fuel additives Neural networks Thermal efficiency Bio-energy Biomass-based fuels Compression ignition engine Lather fat oil Nano additives NOx emissions Peel oil Synthesised Waste biomass Waste to energy Diesel fuels This study aims to derive bioenergy from waste lather fat and citronella grass. Lather fat oil (LFO), citronella grass oil (CGO), a mixture of leather fat oil and citronella grass oil (LFCGO), and a nano-additive-incorporated mixture of lather fat oil and citronella grass oil (NFCO) were synthesized and used in diesel engines as the novelty of this study. ASTM standards were used to investigate and guarantee the fuel's properties. According to the experimental report, the nanoadditive's brake thermal efficiency and brake-specific fuel consumption were more comparable to diesel fuel. Compared to diesel, the NFCO blend reduced hydrocarbon, carbon monoxide, and particulate emissions by 6.48%, 12.33%, and 16.66%, respectively, while carbon dioxide and oxides of nitrogen emissions increased. The experiment's outcomes were verified using an artificial neural network (ANN). The trained model exhibits a remarkable coefficient of determination of 98%, with high R values varying from 0.9075 to 0.9998 and low mean absolute percentage error values ranging from 0.97% to 4.24%. Based on the experimental findings and validation report, it can be concluded that NFCO is an efficient diesel fuel substitute. � 2023 The Authors Final 2024-10-14T03:18:03Z 2024-10-14T03:18:03Z 2023 Article 10.1016/j.psep.2023.07.085 2-s2.0-85166779614 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85166779614&doi=10.1016%2fj.psep.2023.07.085&partnerID=40&md5=632146e4bac7592d56ea15be0b3bfab5 https://irepository.uniten.edu.my/handle/123456789/34122 177 1234 1248 All Open Access Hybrid Gold Open Access Institution of Chemical Engineers Scopus |
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Lather fat oil Nano additive NOx emission Peel oil Waste to energy ASTM standards Biodiesel Biomass Brakes Carbon dioxide Carbon monoxide Diesel engines Fuel additives Neural networks Thermal efficiency Bio-energy Biomass-based fuels Compression ignition engine Lather fat oil Nano additives NOx emissions Peel oil Synthesised Waste biomass Waste to energy Diesel fuels |
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Lather fat oil Nano additive NOx emission Peel oil Waste to energy ASTM standards Biodiesel Biomass Brakes Carbon dioxide Carbon monoxide Diesel engines Fuel additives Neural networks Thermal efficiency Bio-energy Biomass-based fuels Compression ignition engine Lather fat oil Nano additives NOx emissions Peel oil Synthesised Waste biomass Waste to energy Diesel fuels Ramalingam K. Venkatesan E.P. Vellaiyan S. Mukhtar A. Sharifpur M. Yasir A.S.H.M. Saleel C.A. Substitution of diesel fuel in conventional compression ignition engine with waste biomass-based fuel and its validation using artificial neural networks |
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This study aims to derive bioenergy from waste lather fat and citronella grass. Lather fat oil (LFO), citronella grass oil (CGO), a mixture of leather fat oil and citronella grass oil (LFCGO), and a nano-additive-incorporated mixture of lather fat oil and citronella grass oil (NFCO) were synthesized and used in diesel engines as the novelty of this study. ASTM standards were used to investigate and guarantee the fuel's properties. According to the experimental report, the nanoadditive's brake thermal efficiency and brake-specific fuel consumption were more comparable to diesel fuel. Compared to diesel, the NFCO blend reduced hydrocarbon, carbon monoxide, and particulate emissions by 6.48%, 12.33%, and 16.66%, respectively, while carbon dioxide and oxides of nitrogen emissions increased. The experiment's outcomes were verified using an artificial neural network (ANN). The trained model exhibits a remarkable coefficient of determination of 98%, with high R values varying from 0.9075 to 0.9998 and low mean absolute percentage error values ranging from 0.97% to 4.24%. Based on the experimental findings and validation report, it can be concluded that NFCO is an efficient diesel fuel substitute. � 2023 The Authors |
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57307229100 |
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57307229100 Ramalingam K. Venkatesan E.P. Vellaiyan S. Mukhtar A. Sharifpur M. Yasir A.S.H.M. Saleel C.A. |
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Article |
author |
Ramalingam K. Venkatesan E.P. Vellaiyan S. Mukhtar A. Sharifpur M. Yasir A.S.H.M. Saleel C.A. |
author_sort |
Ramalingam K. |
title |
Substitution of diesel fuel in conventional compression ignition engine with waste biomass-based fuel and its validation using artificial neural networks |
title_short |
Substitution of diesel fuel in conventional compression ignition engine with waste biomass-based fuel and its validation using artificial neural networks |
title_full |
Substitution of diesel fuel in conventional compression ignition engine with waste biomass-based fuel and its validation using artificial neural networks |
title_fullStr |
Substitution of diesel fuel in conventional compression ignition engine with waste biomass-based fuel and its validation using artificial neural networks |
title_full_unstemmed |
Substitution of diesel fuel in conventional compression ignition engine with waste biomass-based fuel and its validation using artificial neural networks |
title_sort |
substitution of diesel fuel in conventional compression ignition engine with waste biomass-based fuel and its validation using artificial neural networks |
publisher |
Institution of Chemical Engineers |
publishDate |
2024 |
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1814061166909980672 |
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13.222552 |