eprintid: 10186111
rev_number: 7
eprint_status: archive
userid: 699
dir: disk0/10/18/61/11
datestamp: 2024-01-25 08:47:19
lastmod: 2024-01-25 08:47:19
status_changed: 2024-01-25 08:47:19
type: article
metadata_visibility: show
sword_depositor: 699
creators_name: Hu, Jinwei
creators_name: Zhu, Kewei
creators_name: Cheng, Sibo
creators_name: Kovalchuk, Nina M
creators_name: Soulsby, Alfred
creators_name: Simmons, Mark JH
creators_name: Matar, Omar K
creators_name: Arcucci, Rossella
title: Explainable AI models for predicting drop coalescence in microfluidics device
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F43
keywords: Explainable AI; Drop coalescence; 
Machine learning; LIME; 
SHAP value
note: 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/).
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.
date: 2024-02-01
date_type: published
publisher: Elsevier BV
official_url: http://dx.doi.org/10.1016/j.cej.2023.148465
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 2141638
doi: 10.1016/j.cej.2023.148465
lyricists_name: Zhu, Kewei
lyricists_id: KZHUA89
actors_name: Zhu, Kewei
actors_id: KZHUA89
actors_role: owner
full_text_status: public
publication: Chemical Engineering Journal
volume: 481
article_number: 148465
issn: 1385-8947
citation:        Hu, Jinwei;    Zhu, Kewei;    Cheng, Sibo;    Kovalchuk, Nina M;    Soulsby, Alfred;    Simmons, Mark JH;    Matar, Omar K;           Hu, Jinwei;  Zhu, Kewei;  Cheng, Sibo;  Kovalchuk, Nina M;  Soulsby, Alfred;  Simmons, Mark JH;  Matar, Omar K;  Arcucci, Rossella;   - view fewer <#>    (2024)    Explainable AI models for predicting drop coalescence in microfluidics device.                   Chemical Engineering Journal , 481     , Article 148465.  10.1016/j.cej.2023.148465 <https://doi.org/10.1016/j.cej.2023.148465>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10186111/1/Explainable%20AI%20models%20for%20predicting%20drop%20coalescence%20in%20microfluidics%20device.pdf