Impact of Granular Activated Carbon on Anaerobic Process and Microbial Community Structure during Mesophilic and Thermophilic Anaerobic Digestion of Chicken Manure

In this work, the impact of granular activated carbon (GAC) on the mesophilic and thermophilic anaerobic digestion of chicken manure and the structure of microbial communities was investigated. These results demonstrated that GAC supplementation effectively enhanced the consumption of produced organic acids in the mesophilic and thermophilic batch tests, accompanied by faster biomethane production in the presence of GAC than from reactors without GAC. However, since the free ammonia level was 3–6 times higher in the thermophilic reactors, this led to the instability of the anaerobic digestion process of the nitrogen-rich substrate at thermophilic temperatures. Bacteroidia and Clostridia were the two main bacterial classes in the mesophilic reactors, whereas the class Clostridia had a competitive advantage over other groups in the thermophilic systems. The archaeal communities in the mesophilic reactors were mainly represented by representatives of the genera Methanosarcina, Methanobacterium, and Methanotrix, whereas the archaeal communities in the thermophilic reactors were mainly represented by members of the genera Methanosarcina, Methanoculleus, and Methanothermobacter. New data obtained in this research will help control and manage biogas reactors in the presence of GAC at different temperatures.

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

Granular activated carbon (GAC) adsorption is frequently considered to control recalcitrant organic micropollutants (MPs) in both drinking water and wastewater. To predict full-scale GAC adsorber performance, bench- and/or pilot- scale studies are widely used. These studies have generated a wealth of MP breakthrough curves. The overarching aim of this research was to develop machine learning (ML) models from these data to predict MP breakthrough from adsorbent, adsorbate, and background water matrix properties. These models provide a simple and fast tool to predict GAC performance. To develop information for model calibration, MP breakthrough curves were collected from the peer-reviewed literature, research reports, and engineering reports. These data sets, which included results from rapid small-scale column tests (RSSCTs) and pilot/full-scale adsorbers, were analyzed to determine the bed volumes of water that could be treated until MP breakthrough reached ten percent of the influent MP concentration (BV10). The data set encompassed 43 MPs (including neutral and ionizable organic compounds), 3 GAC types by base material (18 unique GAC products), and 38 water matrices, including groundwater, surface water, and treated wastewater. Approximately 400 data sets were split into training, validation, and test sets. Seventeen candidate features, such as MP properties (Abraham parameters), background water matrix characteristics, and GAC properties, were explored in ML models to predict log-10-transformed BV10 (logBV10). BV10 values obtained from the resulting predictive model were highly correlated with experimentally determined BV10 values (coefficient of determination ~0.89 for logBV10 prediction), and the most effective model predicted BV10 with an absolute mean error of ~ 0.11 log units. Key drivers influencing BV10 prediction included the MP’s partitioning coefficient between air and hexadecane (Abraham parameter L); dissolved organic matter concentration in background water matrix; and the adsorbent’s point of zero charge (pzc). The model can be used to estimate GAC bed life and select effective GACs for the removal of MPs such as per- and polyfluoroalkyl substances (PFASs), pesticides, pharmaceuticals, and volatile organic compounds (VOCs) in a wide range of water types.