Methodology for adaptive decision--making research on manufacturing process of traditional Chinese medicine based on deep reinforcement learning.
10.19540/j.cnki.cjcmm.20220705.304
- Author:
Qi-Long XUE
1
;
Kun-Hong MIAO
1
;
Yang YU
1
;
Zheng LI
2
Author Information
1. College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Traditional Chinese Medicine Tianjin 301617, China State Key Laboratory for Component-based Chinese Medicine Co-founded by Province and MOST Tianjin 301617, China.
2. College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Traditional Chinese Medicine Tianjin 301617, China State Key Laboratory for Component-based Chinese Medicine Co-founded by Province and MOST Tianjin 301617, China Haihe Laboratory of Modern Chinese Medicine Tianjin 301617, China.
- Publication Type:Journal Article
- Keywords:
deep reinforcement learning;
intelligent manufacturing;
process optimization;
self-decision;
traditional Chinese medicine manufacturing
- MeSH:
Medicine, Chinese Traditional;
Drugs, Chinese Herbal;
Artificial Intelligence;
Quality Control;
Algorithms
- From:
China Journal of Chinese Materia Medica
2023;48(2):562-568
- CountryChina
- Language:Chinese
-
Abstract:
The manufacturing process of traditional Chinese medicine is subject to material fluctuation and other uncertain factors which usually cause non-optimal state and inconsistent product quality. Therefore, it is necessary to design and collect the quality-rela-ted physical parameters, process parameters, and equipment parameters in the whole manufacturing process of traditional Chinese medicine for digitization and modeling of the process. In this paper, a method for non-optimal state identification and self-recovering regulation was developed for active quality control in the manufacturing process of traditional Chinese medicine. Moreover, taking vacuum belt drying process as an example, a DQN algorithm-based intelligent decision model was established and verified and the implementation process was also discussed and studied. Thus, the process parameters-based self-optimization strategy discovery and path planning of optimal process control were rea-lized in this study. The results showed that the deep reinforcement learning-based artificial intelligence technology was helpful to improve the product quality consistency, reduce production cost, and increase benefit.
