Jitchaiyapoom, T.; Panjapornpon, C.; Bardeeniz, S.; Hussain, M.A. Production Capacity Prediction and Optimization in the Glycerin Purification Process: A Simulation-Assisted Few-Shot Learning Approach. Processes2024, 12, 661.
Jitchaiyapoom, T.; Panjapornpon, C.; Bardeeniz, S.; Hussain, M.A. Production Capacity Prediction and Optimization in the Glycerin Purification Process: A Simulation-Assisted Few-Shot Learning Approach. Processes 2024, 12, 661.
Jitchaiyapoom, T.; Panjapornpon, C.; Bardeeniz, S.; Hussain, M.A. Production Capacity Prediction and Optimization in the Glycerin Purification Process: A Simulation-Assisted Few-Shot Learning Approach. Processes2024, 12, 661.
Jitchaiyapoom, T.; Panjapornpon, C.; Bardeeniz, S.; Hussain, M.A. Production Capacity Prediction and Optimization in the Glycerin Purification Process: A Simulation-Assisted Few-Shot Learning Approach. Processes 2024, 12, 661.
Abstract
Chemical process control relies on a tightly controlled, narrow range of margins for critical variables, ensuring process stability and safeguarding equipment from potential accidents. The availability of historical process data is limited to a specific setpoint of operation. This challenge raises issues for process monitoring in predicting and adjusting to deviations outside of the range of operational parameters. Therefore, this paper proposes simulation-assisted deep transfer learning for predicting and optimizing the final purity and production capacity of the glycerin purification process. The proposed network is trained by the simulation domain to generate a base feature extractor, which is then fine-tuned using few-shot learning techniques on the target learner to extend the working domain of the model beyond the historical practice. The result shows that the proposed model improved prediction performance by 99% in predicting water content and 79.72% in glycerin prediction over the conventional deep learning model. Additionally, the implementation of the proposed model identified the production and product quality improvement for enhancing the glycerin purification process.
Keywords
glycerin purification; few-shot learning; production optimization; simulation-assisted.
Subject
Engineering, Chemical Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
