Combination of ensemble machine learning models in photocatalytic studies using nano TiO2 - Lignin based biochar

Abstract

Synergizing photocatalytic reactions with machine learning methods can effectively optimize and automate the remediation of pollutants. In this work, commercial Degussa TiO₂ nanoparticles and lignin based biochar (LB) where used to prepare TiO₂: lignin based biochar (TLB) composites using ultrasound-assisted co-precipitation method. The photocatalytic property of the TLB composites where studied by conducting the photocatalytic degradation of a Basic blue 41 (BB41) dye. The influence of calcination temperature, T:LB compositions, catalyst dosage, initial dye pH, initial dye concentration, and illumination time on photocatalytic dye degradation were experimentally studied. The degradation efficiency of 96.72 % was obtained under optimized conditions for the photocatalyst calcined at 500 °C containing a 1:1 wt percentage of TiO₂ and LB. The experimental data was further used to predict the photocatalytic degradation efficiency using Gradient Tree Boosting (GTB) and Extra Trees (ET) models. The GTB model gave the highest prediction accuracy of 94 %. The permutation variable importance revealed catalyst dosage and dye concentration as the most influential parameters in the prediction of the photocatalytic dye degradation efficiency.

Keywords: Basic blue 41 dye; Biochar; Lignin; Machine learning; Photocatalysis; TiO(2).