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

ABSTRACT: In this study, machine learning (ML) algorithms were employed to predict the pseudo-1st-order reaction rate constants for the sonochemical degradation of aqueous organic pollutants under various conditions. A total of 618 sets of data, including ultrasonic, solution, and pollutant characteristics, were collected from 89 previous studies. Considering the difference between the electrical power (P_ele) and calorimetric power (P_cal), the collected data were divided into two groups: data with P_ele and data with P_cal. Eight input variables, including frequency, power density, pH, temperature, initial concentration, solubility, vapor pressure, and octanol-water partition coefficient (K_ow), and one target variable of the degradation rate const

SUBMITTER: Na I 

PROVIDER: S-EPMC11386492 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature