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| The Study on Proximal Policy Optimization Algorithm for Dryer Clusters |
| LIU Yujie1,WANG Jian2, FENG Guoliang3
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| 1. School of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin City 132022, China; 2. Hefei Visionox Electronics Co.Ltd Hefei City 230012, China; 3. School of Automation Engineering, Northeast Electric Power University, Jilin City 132012, China |
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Abstract This study investigates the scheduling problem of operational states within a cluster of dryers. The dryer cluster consists of multiple dryers, where each dryer must perform a regeneration operation after completing its drying tasks. There is a cooperative relationship between the dryers, where the regeneration process has a certain time flexibility after the drying operation of one tank is completed. Given the impact of China’s time-of-use electricity pricing policy, significant differences in electricity costs exist between different time periods. By adjusting the timing of the regeneration operation to suitable periods, it is possible to meet production requirements while reducing electricity expenses. To address the challenge of training models for dryer clusters, this paper proposes an electricity scheduling model for dryer cluster regeneration based on a Markov decision process. To optimize the scheduling of the dryer cluster, a method using the Proximal Policy Optimization (PPO) algorithm is introduced for training the dryer cluster. Experimental validation was conducted using a cluster of seven dryers under different working conditions. The results show that the fewer tanks involved in the drying operation, the more significant the optimization effect. After 240 hours of continuous operation with different numbers of tanks performing drying tasks, electricity costs were reduced by more than 20% compared to the original costs.
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Published: 11 July 2026
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