TY  - JOUR
T1  - PFD Simulator based Deep Reinforcement Learning for Energy Consumption Minimization of Electric RTO
AU  - Mo, Ji-Sik 
AU  - Kim, Myung-Ho 
JO  - Journal of KIISE, JOK
PY  - 2025
DA  - 2025/1/14
DO  - 10.5626/JOK.2025.52.6.490
KW  - electric regenerative thermal oxidizer
KW  - deep q-network
KW  - energy consumption minimization
KW  - optimal auto-control
AB  - This study proposes a method that could generate data through a simulator in situations where data collection is difficult. A deep reinforcement learning agent is then trained based on generated data to maintain stable electric regenerative thermal oxidizer (RTO) operation and minimize energy consumption. First, data were generated from a simulator created using actual equipment Process Flow Diagrams (PFDs) and field operation methods. An environment that incorporated states, actions, and rewards was established for agent training. Performance evaluation results demonstrated that the control using the deep reinforcement learning agent trained with this method enabled more stable operation of the electric RTO system, while simultaneously reducing power consumption by up to approximately 9% compared to the conventional operation strategy.