Machine Learning Models to Predict Early Breakthrough of Recalcitrant Organic Micropollutants in Granular Activated Carbon Adsorbers

Granular activated carbon (GAC) adsorption
is frequently used to
remove recalcitrant organic micropollutants (MPs) from water. The
overarching aim of this research was to develop machine learning (ML)
models to predict GAC performance from adsorbent, adsorbate, and background
water matrix properties. For model calibration, MP breakthrough curves
were compiled and analyzed to determine the bed volumes of water that
can be treated until MP breakthrough reaches ten percent of the influent
MP concentration (BV10). Over 400 data points were split into training,
validation, and testing sets. Seventeen variables describing MP, background
water matrix, and GAC properties were explored in ML models to predict
log10-transformed BV10 values. Using the ML models on the
testing set, predicted BV10 values exhibited mean absolute errors
of ∼0.12 log units and were highly correlated with experimentally
determined values (R2 ≥ 0.88).
The top three drivers influencing BV10 predictions were the air-hexadecane
partition coefficient and hydrogen bond acidity (Abraham parameters L and A) of the MPs and the dissolved organic
carbon concentration of the GAC influent water. The model can be used
to rapidly estimate the GAC bed life, select effective GAC products
for a given treatment scenario, and explore the suitability of GAC
treatment for remediating emerging MPs.