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Abstract: Influence of alternative fuels on diesel engine exhaust particle emission was investigated using an ultra-low sulfur diesel fuel as a baseline fuel where two biodiesels (canola & tallow), Fischer–Tropsch and bioethanol were used as alternative fuel. Both the biodiesels coming from canola and tallow feedstocks, as well as F-T were used as 100% to run the engine where up to 40% energy substitution by ethanol was achieved without any sacrifice of engine power output. It was found that up to 30% ethanol substitution reduced both particulate mass (PM) and particle number (PN) emission consistently for all load settings at 2000rpm, highest 59% reduction in PM and 70% reduction in PN observed at 100% load. As previously suggested the possible mechanism for the observed reduction is the oxidation of particulate matter by OH radicals which are in excess with ethanol fumigation. For 40% ethanol substitution some inconsistency was observed for PM emission at different loads but consistent reduction was found for PN. Condensation of unburned/partially burned hydrocarbons that later condense on existing soot might be responsible for this, as the maximum increase of PM was observed at quarter load where low in cylinder temperature favour to nucleation of unburned hydrocarbons. PM emission was also reduced in case of using 100% FT, and 100% biodiesel and the highest 90% reduction in PM was observed for biodiesel at 100% load with almost no difference between the two biodiesels itself. On the other side a considerable difference was observed between canola and tallow biodiesel in case of PN emission. Canola biodiesel increased PN, due to the presence of the nucleation mode, for almost an order of magnitude for all load and speed settings where no such increase was observed for tallow biodiesel. [Copyright &y& Elsevier]

Biodiesel will provide a significant renewable energy source for transportation in the near future. In the present study, principal component analysis (PCA) has been used to understand the relationship between important properties of biodiesel and its chemical composition. Finally, several artificial intelligence-based models were developed to predict specific biodiesel properties based on their chemical composition. The experimental study was conducted in order to generate training data for the artificial neural network (ANN). Available (experimental) data from the literature was also employed for this modeling strategy. The analytical part of this study found a complex multi-dimensional correlation between chemical composition and biodiesel properties. Average numbers of double bonds in the chemical structure (representing the unsaturated component in biodiesel) and the poly-unsaturated component in biodiesel had a great impact on biodiesel properties. The simulation result in this study demonstrated that ANN is a useful tool for investigating the fuel properties from its chemical composition which eventually can replace the time consuming and costly experimental test. • Comprehensive study of biodiesels' chemical compositions and quality indicators. • Correlated the relationship between chemical composition and biodiesel properties. • Develop an innovative model for biodiesel quality investigation. • Utilising artificial intelligent for discovery sustainable renewable energy. [ABSTRACT FROM AUTHOR]

Diethylene glycol dimethyl ether (DGM), also known as diglyme, has a very high cetane number with fuel-bound oxygen of up to 36%, it has strong potential to reduce diesel emissions. This work is an investigation of the turbocharged diesel engine's emissions and performance parameters using coconut biodiesel-diglyme-diesel blends. Coconut biodiesel was used as an oxygenated fuel, while DGM was utilised as an oxygenated additive for its excellent cetane number and higher fuel-bound oxygen. The reason for adding diglyme to coconut biodiesel blends is to study the influence of cetane number and fuel-bound oxygen on performance, combustion and emission characteristics. There were five fuels tested in this study. To compare the performance, combustion and emissions data, a regular diesel was used as a base fuel. The neat diesel (100% diesel) and the neat coconut biodiesel (100% coconut biodiesel) were designated as diesel and Ox4 respectively. Blend of 70% diesel + 30% coconut biodiesel is abbreviated as Ox1, 70% diesel + 20% coconut biodiesel + 10% diglyme blend is abbreviated as Ox2, and 70% diesel + 10% coconut biodiesel + 20% diglyme blend is labelled as Ox3. All blending percentages were based on a volume basis. Engine experiments were performed with an unmodified Cummins 6-cylinder common rail diesel engine. The engine was fitted with precision measuring instruments. Using GT-Power, a one-dimensional (1-D) model was developed to examine some key performance parameters with those of experimental data. Most of the performance results show the variations between experimental and simulation data were within 10%. [ABSTRACT FROM AUTHOR]

Oxidative potential (OP) is related to the organic phase, specifically to its oxygenated organic fraction (OOA). Furthermore, the oxygen content of fuel molecules has significant influence on particulate OP. Thus, this study aimed to explore the actual dependency of the OOA and ROS to the oxygen content of the fuel. In order to reach the goal, different biodiesels blends, with various ranges of oxygen content; have been employed. The compact time of flight aerosol mass spectrometer (c-ToF AMS) enabled better identification of OOA. ROS monitored by using two assays: DTT and BPEA-nit. Despite emitting lower mass, both assays agreed that oxygen content of a biodiesel is directly correlated with its OOA, and highly related to its OP. Hence, the more oxygen included in the considered biodiesels, the higher the OP of PM emissions. This highlights the importance of taking oxygen content into account while assessing emissions from new fuel types, which is relevant from a health effects standpoint. [ABSTRACT FROM AUTHOR]

Highlights: [•] Inter-cycle variations have been tested for biodiesels and their blends with diesel. [•] Kernel density estimation is used to estimate the key cyclic variation parameters. [•] Correlations between the inter-cycle variabilities and oxygen ratio are shown. [•] The biodiesel molecular profiles affect the cyclic variations significantly. [•] Number of particle per mass unit of biodiesels is much higher than that of diesel. [Copyright &y& Elsevier]