Neural networks for small scale ORC optimization | Zendy

Alessandro Massimiani | Zendy; Laura Palagi | Zendy; Enrico Sciubba | Zendy; Lorenzo Tocci | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Neural networks for small scale ORC optimization

Author(s) -

Alessandro Massimiani,

Laura Palagi,

Enrico Sciubba,

Lorenzo Tocci

Publication year - 2017

Publication title -

energy procedia

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.474

H-Index - 81

ISSN - 1876-6102

DOI - 10.1016/j.egypro.2017.09.174

Subject(s) - organic rankine cycle , heat exchanger , maximization , artificial neural network , working fluid , process engineering , scale (ratio) , waste heat , degree rankine , rankine cycle , computer science , engineering , power (physics) , mathematical optimization , thermodynamics , mathematics , artificial intelligence , mechanical engineering , physics , quantum mechanics

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.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research