Deep Learning Neural Network Approach for Predicting the Sorption of Ionizable and Polar Organic Pollutants to a Wide Range of Carbonaceous Materials

Most
contaminants of emerging concern are polar and/or ionizable
organic compounds, whose removal from engineered and environmental
systems is difficult. Carbonaceous sorbents include activated carbon,
biochar, fullerenes, and carbon nanotubes, with applications such
as drinking water filtration, wastewater treatment, and contaminant
remediation. Tools for predicting sorption of many emerging contaminants
to these sorbents are lacking because existing models were developed
for neutral compounds. A method to select the appropriate sorbent
for a given contaminant based on the ability to predict sorption is
required by researchers and practitioners alike. Here, we present
a widely applicable deep learning neural network approach that excellently
predicted the conventionally used Freundlich isotherm fitting parameters
log KF and n (R2 > 0.98 for log KF, and R2 > 0.91 for n). The neural network models are based on parameters generally available
for carbonaceous sorbents and/or parameters freely available from
online databases. A freely accessible graphical user interface is
provided.