Computational simulation of organic pollutant removal from wastewater by different porous boron nitride (PBN) nanoclusters

Abstract

A computational modeling framework was developed to evaluate the adsorption behavior of boron nitride (BN) for separation of a solute from solution. Adsorption of an organic dye onto the surface of different porous BNs at two different temperatures was computed using different machine learning models. The amount of dye concentration changes in the solution based on the time parameter for different materials has been modeled and validated in this research. An attempt was made to construct the kinetics of adsorption using the developed models rather than the conventional kinetic models. A decision tree (DT), Multilayer Perceptron (MLP), and Linear Regression boosted with Adaboost (ADA-LR) are selected models that are optimized by their hyper-parameters using a grid-search. Models were developed separately for low and high temperatures for three different materials with different chemistry and structures. Using the MSE parameter, DT has an error of 1.44E-02 for low temperatures and 1.48E-02 for high temperatures. It is 1.01E-03 for low and 7.44E-03 for high temperatures using MLP method of simulation. However, in terms of MSE, the best model is ADA-LR, with 4.60E-03 for low temperature and 1.81E-03 for high temperature. With other metrics also, ADA-LR is the best model, so it is selected as the main model of this research. The R2-score of the final model is 0.940 for low and 0.973 for high temperature values.