Viability study of automobile shredder residue as fuel

Car Fluff samples collected from a shredding plant in Italy were classified based on particle size, and three different size fractions were obtained in this way. A comparison between these size fractions and the original light fluff was made from two different points of view: (i) the properties of each size fraction as a fuel were evaluated and (ii) the pollutants evolved when each size fraction was subjected to combustion were studied. The aim was to establish which size fraction would be the most suitable for the purposes of energy recovery. The light fluff analyzed contained up to 50 wt.% fines (particle size < 20 mm). However, its low calorific value and high emissions of polychlorinated dioxins and furans (PCDD/Fs), generated during combustion, make the fines fraction inappropriate for energy recovery, and therefore, landfilling would be the best option. The 50–100 mm fraction exhibited a high calorific value and low PCDD/F emissions were generated when the sample was combusted, making it the most suitable fraction for use as refuse-derived fuel (RDF). Results obtained suggest that removing fines from the original ASR sample would lead to a material product that is more suitable for use as RDF.

Morphological classification of microtidal sand and gravel beaches

A new classification of microtidal sand and gravel beaches with very different morphologies is presented below. In 557 studied transects, 14 variables were used. Among the variables to be emphasized is the depth of the Posidonia oceanica. The classification was performed for 9 types of beaches: Type 1: Sand and gravel beaches, Type 2: Sand and gravel separated beaches, Type 3: Gravel and sand beaches, Type 4: Gravel and sand separated beaches, Type 5: Pure gravel beaches, Type 6: Open sand beaches, Type 7: Supported sand beaches, Type 8: Bisupported sand beaches and Type 9: Enclosed beaches. For the classification, several tools were used: discriminant analysis, neural networks and Support Vector Machines (SVM), the results were then compared. As there is no theory for deciding which is the most convenient neural network architecture to deal with a particular data set, an experimental study was performed with different numbers of neuron in the hidden layer. Finally, an architecture with 30 neurons was chosen. Different kernels were employed for SVM (Linear, Polynomial, Radial basis function and Sigmoid). The results obtained for the discriminant analysis were not as good as those obtained for the other two methods (ANN and SVM) which showed similar success.