Data-driven nonlinear modeling for superheating degree in organic Rankine cycle systems

Organic Rankine Cycle (ORC) power systems have become a promising solution for improving energy efficiency, particularly in waste heat recovery (WHR) applications. These systems convert low-grade heat into electricity, contributing to energy savings and emission reductions. A key control objective is the regulation of the superheating degree, as it directly affects thermodynamic performance and system reliability. This work explores a data-driven modeling strategy using neural networks (NNs) to capture the nonlinear dynamics of the superheating process in a small-scale (11 kWel) ORC unit. To enhance generalization and interpretability, an automatic feature selection framework based on reinforcement learning is developed. The approach evaluates the relevance of multiple candidate input variables, selecting the most informative ones to optimize predictive accuracy. Experimental results show that the proposed model effectively reproduces system behavior while maintaining strong generalization to unseen operating conditions. This modeling framework lays the foundation for advanced control development and contributes to data-driven methodologies for energy systems optimization.