Ricerc@Sapienza

An optimization model based on the use of Neural Network surrogate models for the multi-objective optimization of small scale Organic Rankine Cycles is presented, which couples the optimal selection of the thermodynamic parameters of the cycle with the main design parameters of In-Flow Radial turbines. The proposed approach proved well suited in the resolution of the highly non-linear constrained optimization problems, typical of the design of energy systems.

Dynamic modeling plays a crucial role in the analysis of Organic Rankine Cycle (ORC) systems for waste heat recovery, which deal with a highly unsteady heat source. The efficiency of small scale ORCs (i.e. below 100 kW power output) is low (<10%). Therefore, it is essential to keep the performance of the system as stable as possible. To do so, it is helpful to be able to predict the dynamic behavior of the system, in order to perform a maximization of its performance over the time.

The geothermal sector has a strength point with respect to other renewable energy sources: the availability of a wide range of both thermal and power applications depending on the source temperature. Several researches have been focused on the possibility to produce geothermal energy without brine extraction, by means of a deep borehole heat exchanger. This solution may be the key to increase the social acceptance, to reduce the environmental impact of geothermal projects, and to exploit unconventional geothermal systems, where the extraction of brine is technically complex.

This paper aims at proposing fast and plain design tools to evaluate the best energy application for deep borehole heat exchangers, exploiting geothermal resources. Exergy efficiency has been chosen as a performance index. Five possible utilization solutions have been analyzed: district heating, adsorption cooling, ORC power production, a thermal cascade system, and combined heat and power configuration. An extensive sensitivity analysis on source characteristics and well geometry has been performed to find the design criteria that ensure the maximum exergy performance.