Spent coffee grounds for biodiesel production and other applications

This work evaluates the possibility of using spent coffee grounds (SCG) for biodiesel production and other applications. An experimental study was conducted with different solvents showing that lipid content up to 6 wt% can be obtained from SCG. Results also show that besides biodiesel production, SCG can be used as fertilizer as it is rich in nitrogen, and as solid fuel with higher heating value (HHV) equivalent to some agriculture and wood residues. The extracted lipids were characterized for their properties of acid value, density at 15 °C, viscosity at 40 °C, iodine number, and HHV, which are negatively influenced by water content and solvents used in lipid extraction. Results suggest that for lipids with high free fatty acids (FFA), the best procedure for conversion to biodiesel would be a two-step process of acid esterification followed by alkaline transesterification, instead of a sole step of direct transesterification with acid catalyst. Biodiesel was characterized for its properties of iodine number, acid value, and ester content. Although these quality parameters were not within the limits of NP EN 14214:2009 standard, SCG lipids can be used for biodiesel, blended with higher-quality vegetable oils before transesterification, or the biodiesel produced from SCG can be blended with higher-quality biodiesel or even with fossil diesel, in order to meet the standard requirements.

Multilayer Perceptron Neural Networks Training Through Charged System Search and its Application for Non-Technical Losses Detection

The non-technical loss is not a problem with trivial solution or regional character and its minimization represents the guarantee of investments in product quality and maintenance of power systems, introduced by a competitive environment after the period of privatization in the national scene. In this paper, we show how to improve the training phase of a neural network-based classifier using a recently proposed meta-heuristic technique called Charged System Search, which is based on the interactions between electrically charged particles. The experiments were carried out in the context of non-technical loss in power distribution systems in a dataset obtained from a Brazilian electrical power company, and have demonstrated the robustness of the proposed technique against with several others natureinspired optimization techniques for training neural networks. Thus, it is possible to improve some applications on Smart Grids.