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De Pascale, Andrea
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De Pascale
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Andrea
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Andrea De Pascale
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Guidelines and optimization criteria of a machine learning-based methodology for mixture design in ORC systemsMariani, Valerio; Ottaviano, Saverio; De Pascale, Andrea; Cazzoli, Giulio; Branchini, Lisa; BianchiValerio Mariani, Gian Marco; Bianchi, Gian Marcoorking fluid selection in organic Rankine cycles (ORC) is a critical issue in the system design for specific applications. In most cases, pure fluids are employed as working medium, and the choice among commercial products is fundamentally based on the heat source temperature level, on fluid cost and safety requirements. Mixtures of organic fluids present some interesting features. First of all, zeotropic blends can help reducing the irreversibility associated to the isothermal heat transfer of pure working fluids in the evaporator and in the condenser, with potential improvement of the overall conversion efficiency from heat sources with finite capacity. Moreover, the possibility of using fluids with relatively high global warming potential, blended with low-GWP fluids, allows reducing the equivalent carbon emissions (according to the European F-gas regulation), helping the full transition to cleaner compounds. However, there is a lack of organic mixtures specifically optimized for ORC systems, particularly because different applications require fluids with different properties, and the available solution may not be the optimal for the specific case. Due to the large number of molecules available to be combined, the use of numerical algorithms is mandatory to accomplish the mixture formulation task (type, number and amount of each component). In this work, an optimization tool based on a Bayesian statistical inference methodology is implemented and tested, to define the composition of mixtures of different groups of organic fluids (HFC, HFO, HC). The algorithm adopts a mixed exploration-exploitation searching strategy on a solution domain made by the different pure molecules commonly employed. The tool is programmed to fulfil the maximization of an objective function, built from a set of key physical properties and performance indexes of the mixture. The choice of the properties accounted in the optimization (with relative weight) is discussed, and the algorithm is applied to a case study to demonstrate its capability to define a low-GWP blend replacing HFC 134a, maintaining and possibly enhancing the performance.- PERFORMANCE AND ECONOMIC ASSESSMENT OF A THERMALLY INTEGRATED REVERSIBLE HP/ORC CARNOT BATTERY APPLIED TO DATA CENTERSPoletto, Chiara; De Pascale, Andrea; Ottaviano, Saverio; Dumont, Olivier; Ancona, Maria Alessandra; Bianchi, Michelewhen the contribution of waste heat occurs, resulting in thermally integrated energy storages. In this context, since data centers (DCs) convert almost all the supplied electricity into recoverable waste heat, this work aims at investigating the convenience of integrating a reversible Organic Rankine Cycle (ORC)/Heat Pump (HP) Carnot battery with a DC fed by a photovoltaic power plant. For this purpose, a detailed semi-empirical off-design model of a CB is implemented in a rule-based control strategy to manage the ORC/HP operations in the integrated system. In case of renewable production surplus, the exceeding energy can be stored in the CB through the HP, which is thermally integrated with DC waste heat with the double advantage of operating with a higher coefficient of performance and of reducing the required cooling system power. The stored energy is reconverted through the ORC when the electric demand overcomes the renewable production. A sensitivity analysis varying the storage volume and the energy price has been performed to investigate the influence of such parameters on the integrated system performance. The thermal integration allows to reach roundtrip efficiencies around 37 %, although the very low operating temperatures. The CB intervention results to be economically convenient, especially when the average electricity price is high as it occurred during year 2022. The yearly additional gain, with a storage volume of 10 m3, is assessed to be around 8270 €, and the simple payback period is less than 10 years
