Hu, Jinwei;
Zhu, Kewei;
Cheng, Sibo;
Kovalchuk, Nina M;
Soulsby, Alfred;
Simmons, Mark JH;
Matar, Omar K;
(2024)
Explainable AI models for predicting drop coalescence in microfluidics device.
Chemical Engineering Journal
, 481
, Article 148465. 10.1016/j.cej.2023.148465.
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Abstract
In the field of chemical engineering, understanding the dynamics and probability of drop coalescence is not just an academic pursuit, but a critical requirement for advancing process design by applying energy only where it is needed to build necessary interfacial structures, increasing efficiency towards Net Zero manufacture. This research applies machine learning predictive models to unravel the sophisticated relationships embedded in the experimental data on drop coalescence in a microfluidics device. Through the deployment of SHapley Additive exPlanations values, critical features relevant to coalescence processes are consistently identified. Comprehensive feature ablation tests further delineate the robustness and susceptibility of each model. Furthermore, the incorporation of Local Interpretable Model-agnostic Explanations for local interpretability offers an elucidative perspective, clarifying the intricate decision-making mechanisms inherent to each model’s predictions. As a result, this research provides the relative importance of the features for the outcome of drop interactions. It also underscores the pivotal role of model interpretability in reinforcing confidence in machine learning predictions of complex physical phenomena that are central to chemical engineering applications.
| Type: | Article |
|---|---|
| Title: | Explainable AI models for predicting drop coalescence in microfluidics device |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.cej.2023.148465 |
| Publisher version: | http://dx.doi.org/10.1016/j.cej.2023.148465 |
| Language: | English |
| Additional information: | Copyright © 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
| Keywords: | Explainable AI; Drop coalescence; Machine learning; LIME; SHAP value |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Chemical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10186111 |
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