This study concerns a thermodynamic and technical optimization of a small scale Organic Rankine Cycle system for waste heat recovery applications. An Artificial Neural Network (ANN) has been used to develop a thermodynamic model to be used for the maximization of the production of power while keeping the size of the heat exchangers and hence the cost of the plant at its minimum. R1234yf has been selected as the working fluid. The results show that the use of ANN is promising in solving complex nonlinear optimization problems that arise in the field of thermodynamics.
Neural networks for small scale ORC optimization / Massimiani, Alessandro; Palagi, Laura; Sciubba, Enrico; Tocci, Lorenzo. - In: ENERGY PROCEDIA. - ISSN 1876-6102. - STAMPA. - 129:(2017), pp. 34-41. (Intervento presentato al convegno 4th International Seminar on Organic Rankine Cycle (ORC) Power Systems, ORC 2017 tenutosi a Milano; Italy) [10.1016/j.egypro.2017.09.174].
Neural networks for small scale ORC optimization
Alessandro Massimiani;Laura Palagi;Enrico Sciubba;Lorenzo Tocci
2017
Abstract
This study concerns a thermodynamic and technical optimization of a small scale Organic Rankine Cycle system for waste heat recovery applications. An Artificial Neural Network (ANN) has been used to develop a thermodynamic model to be used for the maximization of the production of power while keeping the size of the heat exchangers and hence the cost of the plant at its minimum. R1234yf has been selected as the working fluid. The results show that the use of ANN is promising in solving complex nonlinear optimization problems that arise in the field of thermodynamics.| File | Dimensione | Formato | |
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