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PFD Simulator based Deep Reinforcement Learning for Energy Consumption Minimization of Electric RTO
http://doi.org/10.5626/JOK.2025.52.6.490
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.
An Energy-Efficient HVAC Control Scheme Based on Deep Reinforcement Learning Using Liquefied Natural Gas Carrier Environment Prediction Model
Youngeun Chae, Jaeseong Kim, Jin-Sung Ok, Young-Kyoon Suh
http://doi.org/10.5626/JOK.2022.49.12.1062
This paper proposes a heating, ventilation, and air conditioning (HVAC) control scheme based on deep reinforcement learning to stably maintain the internal environment of an LNG cargo hold under construction and minimize energy consumption. Since a particular environment such as inside of a cargo ship under construction is readily influenced by several factors, it is difficult to accurately forecast indoor temperature and humidity information and automatically control HVAC system. To alleviate this problem, we propose a novel scheme to steadily controlling an indoor environment via an HVAC control agent trained through a deep reinforcement learning model. In this scheme, we construct an indoor-environment state prediction model based on correlational analyses of collected data without expertise concerning the operating circumstance, define the state and action based on the model, and then build the agent trained with a policy through a reward function. To assess the validity of the proposed scheme, we conduct HVAC control performance evaluation in a simulated environment built using the data collected from an actual LNGC HVAC system. Our simulation results show that the Double Deep Q-Network (DQN) model was the most effective for HVAC control among three types of reinforcement learning models that we considered in this study. Also, the results reveal that the trained agent could reduce average daily power consumption by 28.2% while stabilizing indoor environment of the cargo hold within user-specified temperature range.
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