A ranking scheme for biodiesel underpinned by critical physicochemical properties

Rahman, SMA; Fattah, IMR; Maitra, S; Mahlia, TMI

A ranking scheme for biodiesel underpinned by critical physicochemical properties

Rahman, SMA Fattah, IMR Maitra, S Mahlia, TMI

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Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: Energy Conversion and Management, 2021, 229, pp. 113742-113742
Issue Date:: 2021-02-01

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Field	Value	Language
dc.contributor.author	Rahman, SMA
dc.contributor.author	Fattah, IMR
dc.contributor.author	Maitra, S
dc.contributor.author	Mahlia, TMI
dc.date.accessioned	2021-02-03T22:19:03Z
dc.date.available	2021-02-03T22:19:03Z
dc.date.issued	2021-02-01
dc.identifier.citation	Energy Conversion and Management, 2021, 229, pp. 113742-113742
dc.identifier.issn	0196-8904
dc.identifier.uri	http://hdl.handle.net/10453/145780
dc.description.abstract	© 2020 Elsevier Ltd Diminishing oil reserve, escalating energy dependence, and the environmental impact of fossil fuel utilization has led to research on renewable energy resources with a cleaner carbon footprint. Biofuel, especially biodiesel, has become a feasible substitute for petroleum diesel as it can be directly used in existing transport infrastructure without significant alteration. This paper starts by discussing some critical physicochemical properties and their effect on engine performance and emission. The research then proposes a ranking scheme to select the most suitable biodiesel based on six vital physicochemical properties: density, viscosity, heating value, flash point, cetane number and oxidation stability. The solution developed is independent of supervision, contrary to popular learning algorithms and can operate on the only intelligence whether an attribute is favourable by its higher/lower values. The novelty of the work consists in ensuring that the rarer properties pick up the greater weights and in establishing a simple ranker based on descriptive statistics. This scheme first generates transactions against each biodiesel which helps in association rule mining, which is later used to score/rank the biodiesels. The three phases and their subordinate sub-steps have been carried out using the platforms: Python, R and Tableau, respectively. The study endorses Brassica juncea, Cardoon (Cynara cardunculu), and poppyseed oil as the most desirable biodiesel feedstocks. On the other hand, cedar, castor and hiptage were ranked as least desirable in the list of 71 feedstocks based on the proposed ranking scheme. The proposed ranking scheme will help decision-makers such to analyze and obtain tailored biodiesel feedstock for their purposes.
dc.language	en
dc.publisher	Elsevier BV
dc.relation.ispartof	Energy Conversion and Management
dc.relation.isbasedon	10.1016/j.enconman.2020.113742
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0906 Electrical and Electronic Engineering, 0913 Mechanical Engineering
dc.subject.classification	Energy
dc.title	A ranking scheme for biodiesel underpinned by critical physicochemical properties
dc.type	Journal Article
utslib.citation.volume	229
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0913 Mechanical Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2023-02-01T00:00:00+1000Z
dc.date.updated	2021-02-03T22:18:59Z
pubs.publication-status	Accepted
pubs.volume	229

Abstract:

© 2020 Elsevier Ltd Diminishing oil reserve, escalating energy dependence, and the environmental impact of fossil fuel utilization has led to research on renewable energy resources with a cleaner carbon footprint. Biofuel, especially biodiesel, has become a feasible substitute for petroleum diesel as it can be directly used in existing transport infrastructure without significant alteration. This paper starts by discussing some critical physicochemical properties and their effect on engine performance and emission. The research then proposes a ranking scheme to select the most suitable biodiesel based on six vital physicochemical properties: density, viscosity, heating value, flash point, cetane number and oxidation stability. The solution developed is independent of supervision, contrary to popular learning algorithms and can operate on the only intelligence whether an attribute is favourable by its higher/lower values. The novelty of the work consists in ensuring that the rarer properties pick up the greater weights and in establishing a simple ranker based on descriptive statistics. This scheme first generates transactions against each biodiesel which helps in association rule mining, which is later used to score/rank the biodiesels. The three phases and their subordinate sub-steps have been carried out using the platforms: Python, R and Tableau, respectively. The study endorses Brassica juncea, Cardoon (Cynara cardunculu), and poppyseed oil as the most desirable biodiesel feedstocks. On the other hand, cedar, castor and hiptage were ranked as least desirable in the list of 71 feedstocks based on the proposed ranking scheme. The proposed ranking scheme will help decision-makers such to analyze and obtain tailored biodiesel feedstock for their purposes.

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http://hdl.handle.net/10453/145780