Potential hybrid feedstock for biodiesel production in the tropics

Recently, mixture of different oils at various proportions have been used as feedstock for biodiesel production. The primary aim is to improve fuel properties which are strongly influenced by the fatty acid composition of the individual oil that makes up the feedstock mix. The tropics are renowned for abundant oil-bearing crops of which palm kernel oil (PKO) from palm seed and groundnut oil (GNO) are prominent. This present paper investigated biodiesel production from hybrid oil (HO) of PKO (medium carbon chain and highly saturated oil) and GNO (long carbon chain and highly unsaturated oil) at 50/50 (v/v) blending. The principal fatty acids (FAs) in the HO are oleic (35.62%) and lauric acids (24.23%) with 47.80% of saturated FA and 52.26% of unsaturated FA contents. The chemical conversion of the oil to methyl ester (ME) gave 86.56% yield. Fuel properties of hybrid oil methyl ester (the HOME) were determined in accordance with standard test methods and were found to comply with both ASTM D6751 and EN 14214 standards. The oxidative stability, cetane number and kinematic viscosity (KV) of HOME were observed to be improved when compared with those of GNO methyl ester from single parent oil, which could be accredited to the improved FA composition of the HO. The KV (3.69 mm²/s) of HOME obtained in this paper was remarkably low compared with those reported in literature for most biodiesels. This value suggests better flow, atomization, spray and combustion of this fuel. Conclusively, the binary blend of oils can be a viable option to improve the fuel properties of biodiesel feedstock coupled with reduced cost.     

Response of multi-step compound pre-equilibrium reaction cross sections for the (p,n) reactions to forms of optical model parameters

In furtherance to improving agreement between calculated and experimental nuclear data, the nuclear reaction code GAMME was used to calculate the multistep compound(MSC) nucleus double differential cross sections(DDCs) for proton-induced neutron emission reactions using the Feshbach-Kerman-Koonin(FKK) formalism. The cross sections were obtained for reactor structural materials involving ~(52)Cr(p, n)~(52)Mn,~(56)Fe(p,n)~(56)Co, and ~(60)Ni(p, n)~(60)Cu reactions at 22.2 MeV incident energy using the zero-range reaction mechanism. Effective residual interaction strength was 28 MeV, and different optical potential parameters were used for the entrance and exit channels of the proton-neutron interactions. The calculated DDCs were fitted to experimental data at the same backward angle of 150°, where the MSC processes dominate. The calculated and experimental data agree well in the region of pre-equilibrium(MSC) reaction dominance against a weaker fit at the lower emission energies. We attribute underestimations to contributions from the other reaction channels and disagreement at higher outgoing energies to reactions to collectively excited states. Contrary to the FKK multi-step direct calculations, contributions from the higher stages to the DDCs are significant. Different sets of parameters resulted in varying levels of agreement of calculated and experimental data for the considered nuclei.     

Prediction of selected biodiesel fuel properties using artificial neural network

Biodiesel is an alternative fuel to replace fossil-based diesel fuel. It has fuel properties similar to diesel which are generally determined experimentally. The experimental determination of various properties of biodiesel is costly, time consuming and a tedious process. To solve these problems, artificial neural network (ANN) has been considered as a vital tool for estimating the fuel properties of biodiesel, especially from its fatty acid (FA) composition. In this study, four ANNs have been designed and trained to predict the cetane number (CN), flash point (FP), kinematic viscosity (KV) and density of biodiesel using ANN with logsig and purelin transfer functions in the hidden layer of all the networks. The five most prevalent FAs from 55 feedstocks found in the literature utilized as the input parameters for the model are palmitic, stearic, oleic, linoleic and linolenic acids except for density network with a sixth parameter (temperature). Other FAs that are present in the biodiesels have been considered based on the number of carbon atom chains and the level of saturation. From this study, the prediction accuracy and the average absolute deviation of the networks are CN (96.69%; 1.637%), KV (95.80%; 1.638%), FP (99.07%; 0.997%) and density (99.40%; 0.101%). These values are reasonably better compared to previous studies on empirical correlations and ANN predictions of these fuel properties found in literature. Hence, the present study demonstrates the ability of ANN model to predict fuel properties of biodiesel with high accuracy.