Meng, Qingbo;
Bogle, David L;
Charitopoulos, Vassilis M;
(2025)
Hedging Against Material Uncertainty via Chance-Constrained Recurrent Neural Networks: A Continuous Pharmaceutical Manufacturing Case Study.
Engineering
, 52
pp. 129-141.
10.1016/j.eng.2025.05.019.
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Abstract
In the pharmaceutical industry, model-based prediction is a crucial stage in process development that allows pharmaceutical companies to simulate different scenarios toward improving process efficiency, reducing costs, and enhancing product quality. Nevertheless, ensuring the quality of formulated pharmaceutical products through the management of raw material variations has always been a challenging task. In this work, data-driven chance-constrained recurrent neural networks (CCRNNs) are developed to address the issue arising from raw material uncertainty. Our goal is to explore how, by proactively incorporating uncertainty into the model training process, more accurate predictions and enhanced robustness can be realized. The proposed approach is tested on a fluid bed dryer (FBD) from a continuous pharmaceutical manufacturing pilot plant. The results demonstrate that CCRNN models offer more robust and accurate predictions for the critical quality attribute (CQA)—in this case, moisture content—when material variations occur, compared with conventional recurrent neural network-based models.
| Type: | Article |
|---|---|
| Title: | Hedging Against Material Uncertainty via Chance-Constrained Recurrent Neural Networks: A Continuous Pharmaceutical Manufacturing Case Study |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.eng.2025.05.019 |
| Publisher version: | https://doi.org/10.1016/j.eng.2025.05.019 |
| Language: | English |
| Additional information: | © The Author(s), 2025. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. https://creativecommons.org/licenses/by/4.0/ |
| Keywords: | Data-driven chance constraints, Recurrent neural networks, Managing material uncertainty, Continuous pharmaceutical manufacturing, Smart manufacturing |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Chemical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10216919 |
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