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Abstract: The study considers the “renovation” (as defined by Italian legislation) of an electricity-generating plant using biogas produced in a managed landfill as the primary energy source. The landfill, located in the Marche region (central Italy), receives about 100 kt y−1 of urban and industrial residues. The plant is endowed with two 470 kW (e) internal combustion engines and has been in operation since 1998. At the end of its lifecycle it is scheduled for decommissioning. Public incentives for energy production from renewable sources, which the plant enjoyed in the first eight years of activity, have also expired. The study examines the main legal, technical and economic options available to the landfill management, in particular considering the new Italian and EU incentives for energy generation from renewable sources. Five configurations are considered for the replacement of the existing engines, three at the original site (a single combustion engine with/without incentives, and a plant with microturbines), and two involving the construction of new plants at a separate site (a cogeneration plant with a combustion engine and one with microturbines). The study provides data that may be a useful basis for other similar cases and for simulations. [Copyright &y& Elsevier]

Abstract: In this work, artificial neural networks (ANNs) were applied to describe the performance of a micro gas turbine (MGT). In particular, they were used (i) to complete performance diagrams for unavailable experimental data; (ii) to assess the influence of ambient parameters on performance; and (iii) to analyze and predict emissions of pollutants in the exhausts. The experimental data used to feed the ANNs were acquired from a manufacturer’s test bed. Though large, the data set did not cover the whole working range of the turbine; ANNs and an artificial neural fuzzy interference system (ANFIS) were therefore applied to fill information gaps. The results of this investigation were also used for sensitivity analysis of the machine’s behavior in different ambient conditions. ANNs can effectively evaluate both MGT performance and emissions in real installations in any climate, the worst R 2 in the validation set being 0.9962. [ABSTRACT FROM AUTHOR]