A ranking scheme for biodiesel underpinned by critical physicochemical properties

• Published in: Energy conversion and management Vol. 229; p. 113742
• Main Authors: Rahman, S.M.A., Fattah, I.M.R., Maitra, S., Mahlia, T.M.I.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.02.2021; Elsevier Science Ltd
• Subjects: Algorithms; Biodiesel; Biodiesel fuels; Biodiesel Ranking; Biofuels; Brass plating; Brassica; Calorific value; Cetane number; Data mining; Decision analysis; Decision making; Diesel; Diesel fuels; Energy resources; Energy sources; Engine emission; Engine performance; Environmental impact; Flash point; Fossil fuels; Intelligence; Learning algorithms; Machine learning; Oxidation; Physicochemical properties; Properties (attributes); Ranking; Raw materials; Renewable energy; Biodiesel Ranking; Engine emission; Biodiesel; Engine performance; Physicochemical properties
• ISSN: 0196-8904; 1879-2227
• DOI: 10.1016/j.enconman.2020.113742

•A novel biodiesel ranking scheme is proposed.•The ranking was carried out using three platforms: Python, R and Tableau.•71 most reported biodiesels were ranked.•Brassica juncea, Cardoon, and poppyseed oil were the most desirable biodiesel feedstocks. 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.