| Title |
Prediction of Microplastic Removal Behavior via Gaussian Process Regression Based Residual Correction of a Reaction Kinetic Model |
| Authors |
Seong-Hun Kim ; Jin-Gyu Kim |
| DOI |
https://doi.org/10.5207/JIEIE.2026.40.3.222 |
| Keywords |
DBD; Gaussian proccess regression; Microplastic; Reaction kinetics; Removal |
| Abstract |
Low-temperature plasma technology has recently gained increasing attention as a promising approach for microplastic degradation. However, most previous studies have primarily focused on removal efficiency and byproduct analysis, with limited emphasis on quantitative prediction of removal behavior. In this study, we proposed a predictive model combining reaction kinetics and Gaussian process regression to describe microplastic removal behavior under varying power and treatment time conditions. The proposed model accurately reproduced the experimental removal behavior and demonstrated reliable predictive capability in untrained power and time domains. Furthermore, the model was used to identify favorable operating conditions for achieving rapid microplastic removal with high energy yield. These findings suggest that the kinetics?Gaussian process regression model can serve as a useful tool for predicting low-temperature plasma-based microplastic removal behavior and for optimizing process operating conditions. |