Machine Learning for the prediction of the dynamic behavior of a small scale ORC system

Laura Palagi, Apostolos Pesyridis, Enrico Sciubba and Lorenzo Tocci

Energy, 2019, vol. 166, issue C, 72-82

Abstract: Dynamic modelling 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.

Keywords: Artificial neural networks; ORC; Dynamic system; Experimental ORC (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:166:y:2019:i:c:p:72-82

DOI: 10.1016/j.energy.2018.10.059

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