Fuel 216 (2018) 322–329 Contents lists available at ScienceDirect Fuel journal homepage: www.elsevier.com/locate/fuel Full Length Article Prediction capabilities of mathematical models in producing a renewable fuel from waste cooking oil for sustainable energy and clean environment a, b A. Avinash , A. Murugesan Department of Mechanical Engineering, KPR Institute of Engineering and Technology, Arasur, Coimbatore 641 407, Tamil Nadu, India Department of Mechanical Engineering, K.S. Rangasamy College of Technology, Tiruchengode 637 215, Tamil Nadu, India GR APHICAL A BSTRACT ARTICLE I NFO ABSTRACT Keywords: The present work describes the comparison of biodiesel yield prediction by Response Surface Methodology Waste cooking oil (RSM) and Artiﬁcial Neural Network (ANN). The prediction models were developed based on three-level design Transesteriﬁcation of experiments conducted with waste cooking oil transesteriﬁed by varying four process parameters such as Biodiesel catalyst concentration, molar ratio, reaction time, and stirrer speed. The optimum reaction conditions were Response surface methodology found to be 0.75% wt/wt catalyst concentration, 9:1 M ratio, 60 min reaction time and 500 rpm stirrer speed. For Artiﬁcial neural network these optimum conditions, experimental fatty acid methyl ester (FAME) content of 95.05 ± 0.26% was ob- tained, which was in good agreement with the predicted yield.
Journal of Cleaner Production – Elsevier
Published: Jul 20, 2018
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera