G. Riva

CARACTERIZACIÓN ENERGÉTICA DEL MARABÚ

Forest biomass marabu (Dichrostachys cinerea) is a wild shrub, that covers a large number of hectares in Cuba and that could become an important source of renewable energy; as biomass was subjected to physical chemical characterization, using specifications techniques of the European Committee for Standardization (CEN), and the main points of the pyrolysis process was identified by thermo-gravimetric analysis to evaluate the feasibility of its use as energy source.The study helped to show that this biomass has suitable characteristics for using as an energy source, a heating value = 19100 kJkg-1, 3,4% of ash and a melting temperature of 1460oC and also low in chlorine and sulfur- The thermo-gravimetric analysis pointed out two areas of well-defi ned pyrolysis reaction: the active zone, dominated by the decomposition of hemi-cellulose and cellulose, and the passive one, marked by the lignin decomposition; actually it occurs in a wide range of temperature, lower rate of degradation and overlapping decomposition of the other components. It was also shown a weight loss of 60% for the former.

Further Analysis And Modelling Of Soil Tare Level In The Sugarbeet Industry By Means Of Neural Networks

The unwanted soil collected during the harvesting of sugarbeets (soil tare) represents one of main by-products of the sugar industry in Europe. This solid waste causes problems in terms of production costs (soil tare could represent 15-20% of the transported product to the processing plant) and environmental impact (around 2 Mt/y of soil waste must be disposed in landfills by 20 processing plants in Italy). In the last few years, various studies have stressed that the factors affecting the soil tare are multiple and dependant on the nature of the soil, the machines utilised to handle the product and the meteorological course. In this study, we utilize different Neural Network predictors to power an online optimization scheme for the harvesting season. The aim of the research is to better understand the relationship between the soil tare in the sugarbeet industry and the meteorological course during the harvest and post harvest period by means of conventional statistical and neural networks analysis. The research was carried out on the basis of hundreds of data relevant to loads of beets (30 t/each in average) processed in a sugarbeet industry operating in Italy (Ancona province) during the last few years. The model obtained could be useful to better program all the harvesting and post harvesting operations before the industrial processing. Simulated results show significant economical and ecological advantages.

Analysis and Modelling of the Relationship between Meteorological Course and Mass of Soil Wastes in the Sugar-Beet Industry

Abstract this work illustrates the results obtained with the application of the artificial neural network (ANN) models for the prediction of soil tare levels of sugar-beet in the course of the harvesting season on the basis of the meteorological patterns (here mainly described by the rainfall). As known, the soil tare affects the costs and the impact on the environment of the sugar production process. Numerous factors are related with the soil tare and their reciprocal relationships are complex. Among the available ANN architectures, the Elman-Jordan and General Regression Neural Network were applied to the 1997 data, collected in the central part of Italy. The results show that the values predicted by the two models were reasonably similar to the real values. However, in the cases where the variability of the soil tare were very high, the chosen models show a lower accuracy.

Optimization Tool for Qualitative Performance Analysis of Agro-Industrial Machinery

Abstract A software package is being developed to simulate generalized Newtonian dynamic physics with the specific aim to help in the design and optimization of agro-industrial machines by reproducing the interactions between mechanical parts and different bodies. Preliminary results of the simulation are under comparison with those obtained with a full-scale cleaning device for a sugar-beet harvester. Development of this software could provide a useful tool aiding in the design of processing machinery.