연구성과

금주의우수논문

SCI-E Article
Statistical analysis, machine learning modeling, and text analytics of aggregation attachment efficiency: Mono and binary particle systems
김현중
  1. 성명김현중()
  2. 소속공과대학 자원환경공학과
  3. 캠퍼스
  4. 우수선정주2023년 06월 1째주
Author
Gomez-Flores, Allan (Dept Earth Resources & Environm Engn); Hong, Gilsang (Dept Earth Resources & Environm Engn); 김현중 (Dept Earth Resources & Environm Engn) corresponding author;
Corresponding Author Info
Kim, H (corresponding author), Hanyang Univ, Dept Earth Resources & Environm Engn, 222 Wangsimni ro, Seoul 04763, South Korea.
E-mail
kshjkim@hanyang.ac.kr
Document Type
Article
Source
JOURNAL OF HAZARDOUS MATERIALS Volume:454 Issue: Pages:- Published:2023
External Information
http://dx.doi.org/10.1016/j.jhazmat.2023.131482
Abstract
The aggregation attachment efficiency (alpha) is the fraction of particle-particle collisions resulting in aggregation. Despite significant research, alpha predictions have not accounted for the full complexity of systems due to constraints imposed by particle types, dispersed matter, water chemistry, quantification methods, and modeling. Experimental alpha values are often case-specific, and simplified systems are used to rule out complexity. To address these challenges, statistical analysis was performed on alpha databases to identify gaps in current knowledge, and machine learning (ML) was used to predict alpha under various particle types and conditions. Moreover, text analytics was employed to support knowledge from statistics and ML, as well as gain insight into the ideas communicated by current literature. Most studies investigated alpha in mono-particle systems, but binary or higher systems require more investigation. Furthermore, our work highlights that numerous variables, interactions, and mechanisms influence alpha behavior, making its investigation complex and difficult for both experiments and modeling. Consequently, future research should incorporate more particle types, shapes, coatings, and surface heterogeneities, and aim to address overlooked variables and conditions. Therefore, building a comprehensive alpha database can enable the development of more accurate empirical models for prediction.
Web of Science Categories
Engineering, Environmental; Environmental Sciences
Funding
Basic Science Program through the National Research Foundation (NRF) of Korea - Ministry of Education [NRF-2021R1I1A1A01054655]; NRF of Korea - Korea government (MSIT) [NRF-2020R1A2C1013851, NRF-2022R1A5A1032539]
Language
English
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