globalchange  > 气候变化事实与影响
DOI: 10.1016/j.renene.2018.08.091
WOS记录号: WOS:000456760900102
论文题名:
Modeling diesel engine fueled with tamanu oil - Diesel blend by hybridizing neural network with firefly algorithm
作者: Rao, Yarrapragada K. S. S.1; Krishna, B. Bala2
通讯作者: Rao, Yarrapragada K. S. S.
刊名: RENEWABLE ENERGY
ISSN: 0960-1481
出版年: 2019
卷: 134, 页码:1200-1212
语种: 英语
英文关键词: Tamanu oil ; Neural model ; Firefly algorithm ; Diesel engine ; Biodiesel
WOS关键词: NONEDIBLE VEGETABLE-OILS ; EMISSION CHARACTERISTICS ; BIODIESEL PRODUCTION ; OXIDATION STABILITY ; COMPRESSION RATIO ; PALM OIL ; PERFORMANCE ; OPTIMIZATION
WOS学科分类: Green & Sustainable Science & Technology ; Energy & Fuels
WOS研究方向: Science & Technology - Other Topics ; Energy & Fuels
英文摘要:

Research works are ongoing in mixing the biologically synthesized oil with the diesel for reducing the effect of global warming and climate change. From the review study, it is noted that the blended biodiesels require more assert about their practical viability. So, the non-edible Tamanu oil is synthesized and it is blended with diesel and its emission characteristics, engine performance and combustion characteristics are studied in our previous work. This paper attempts to model the diesel engine fueled with tamanu oil biodiesel blend. The proposed model exploits the context of neural network and the firefly algorithm is used to train it. After analyzing the various characteristics of the diesel engine, the acquired data is subjected to a proposed FF-NM method. The simulated results are statistically evaluated and the proposed modeling method is proved to be better than the other NM. (C) 2018 Published by Elsevier Ltd.


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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/132948
Appears in Collections:气候变化事实与影响

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作者单位: 1.Aditya Coll Engn, Surampalem 533437, Andhra Pradesh, India
2.Jawaharlal Nehru Technol Univ, Kakinada 533003, Andhra Pradesh, India

Recommended Citation:
Rao, Yarrapragada K. S. S.,Krishna, B. Bala. Modeling diesel engine fueled with tamanu oil - Diesel blend by hybridizing neural network with firefly algorithm[J]. RENEWABLE ENERGY,2019-01-01,134:1200-1212
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