Work

Machine learning model to predict rate constants for sonochemical degradation of organic pollutants

Year: 2024

Type: article

Abstract: ). A significantly higher prediction performance was obtained using data without outliers and augmented data. Consequently, all the applied ML models could be used to predict the sonochemical degradation of aqueous pollutants, and the XGB model showed the highest accuracy in predicting the rate constants. In addition, the power density and frequency were the most influential factors among the eight input variables in prediction with the Shapley additive explanation (SHAP) values method. The degradation rate constants of the two pollutants over a wide frequency range (20-1,000 kHz) were predicted using the trained ML model (XGB) and the prediction results were analyzed.

Source: Ultrasonics Sonochemistry

DOI: 10.1016/j.ultsonch.2024.107032

Authors: Iseul Na, Taeho Kim, Pengpeng Qiu, Younggyu Son

Institutions: Kumoh National Institute of Technology, Donghua University, State Key Laboratory for Modification of Chemical Fibers and Polymer Materials

Language: English

Topic: Ultrasound and Cavitation Phenomena

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

SDG: Clean water and sanitation

Open Access status: gold

Estimated APC paid: $2,700