Control of Superheat of Organic Rankine Cycle under Transient Heat Source Based on Deep Reinforcement Learning

The organic Rankine cycle (ORC) recovers engine waste heat, yet transient operating conditions make superheat control challenging. This work proposes two DRL-based controllers for superheat regulation under a transient heat source, alleviating dependence on disturbance prediction that hampers MPC and DP methods. The DRL controllers are evaluated against baselines, showing strong tracking performance under real-world variability.