The use of artificial neural networks for identifying sustainable biodiesel feedstocks
Jahirul, Mohammed I., Brown, Richard J., Senadeera, Wijitha, O'Hara, Ian M., & Ristovski, Zoran (2013) The use of artificial neural networks for identifying sustainable biodiesel feedstocks. Energies, 6(8), pp. 3764-3806.
Over the past few decades, biodiesel produced from oilseed crops and animal fat is receiving much attention as a renewable and sustainable alternative for automobile engine fuels, and particularly petroleum diesel. However, current biodiesel production is heavily dependent on edible oil feedstocks which are unlikely to be sustainable in the longer term due to the rising food prices and the concerns about automobile engine durability. Therefore, there is an urgent need for researchers to identify and develop sustainable biodiesel feedstocks which overcome the disadvantages of current ones. On the other hand, artificial neural network (ANN) modeling has been successfully used in recent years to gain new knowledge in various disciplines. The main goal of this article is to review recent literatures and assess the state of the art on the use of ANN as a modeling tool for future generation biodiesel feedstocks. Biodiesel feedstocks, production processes, chemical compositions, standards, physio-chemical properties and in-use performance are discussed. Limitations of current biodiesel feedstocks over future generation biodiesel feedstock have been identified. The application of ANN in modeling key biodiesel quality parameters and combustion performance in automobile engines is also discussed. This review has determined that ANN modeling has a high potential to contribute to the development of renewable energy systems by accelerating biodiesel research.
Impact and interest:
Citation counts are sourced monthly from and citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
|Item Type:||Journal Article|
|Keywords:||renewable energy, biodiesel, Artificial Neural Networks (ANN), second generation feedstock|
|Subjects:||Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > AUTOMOTIVE ENGINEERING (090200)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > MECHANICAL ENGINEERING (091300)
|Divisions:||Current > Schools > School of Chemistry, Physics & Mechanical Engineering
Current > Research Centres > Centre for Tropical Crops and Biocommodities
Past > QUT Faculties & Divisions > Faculty of Science and Technology
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2013 [please consult the author]|
|Deposited On:||30 Jul 2013 22:49|
|Last Modified:||01 Aug 2013 07:51|
Repository Staff Only: item control page