Model-based economic analysis under uncertainty for PFAS treatment by granular activated carbon and ion exchange technologies

Recent drinking water regulations have imposed remediation for per- and polyfluoroalkyl substances (PFAS). In response, treatment facilities may be required to retrofit existing treatment schemes to treat PFAS below maximum contaminant levels (MCLs). Adsorption technologies such as granular activated carbon (GAC) and ion exchange (IX) have been demonstrated to be effective; however, there are limited techno-economic metrics available that provide guidance on technology selection and design for diverse PFAS-containing source water conditions. Process systems engineering (PSE) tools that traditionally perform these analyses are hindered by the data availability, model validity, and understanding of treatment phenomena for emerging contaminants. This work employs published data regressions, statistical models, process models, techno-economic analyses, and other process systems tools in a model-based uncertainty framework to consider the limitations of emerging contaminant research. Through this analysis framework, economic results are provided as probabilistic distributions based on the uncertainty of the models and diverse conditions that treatment facilities experience. Regressed parameter distributions and model predictive performance trends for each technology are identified based on PFAS structure and chain length. GAC systems are evaluated at consistently lower levelized costs of water (LCOWs) with less economic risk over IX systems considering uncertainty across most design conditions and PFAS species. Both technologies are evaluated to have comparable adsorbent usage intensity on a volume basis, indicative of similar sustainability.