Machine learning-assisted optimization of TBBPA-bis-(2,3-dibromopropyl ether) extraction process from ABS polymer

Abstract

The increasing amount of e-waste plastics needs to be disposed of properly, and removing the brominated flame retardants contained in them can effectively reduce their negative impact on the environment. In the present work, TBBPA-bis-(2,3-dibromopropyl ether) (TBBPA-DBP), a novel brominated flame retardant, was extracted by ultrasonic-assisted solvothermal extraction process. Response Surface Methodology (RSM) achieved by machine learning (support vector regression, SVR) was employed to estimate the optimum extraction conditions (extraction time, extraction temperature, liquid to solid ratio) in methanol or ethanol solvent. The predicted optimum conditions of TBBPA-DBP were 96 min, 131 mL g(-1), 65 degrees C, in MeOH, and 120 min, 152 mL g(-1), 67 degrees C in EtOH. And the validity of predicted conditions was verified.