CAREER: Leveraging Chemical Demulsifiers to Elucidate Mechanisms of Membrane Demulsification and Mitigate Membrane Fouling for Oily Wastewater Treatment

Freshwater supplies are under pressure due to scarcity and increasing energy production. Oil and gas extraction and related industries produce large amounts of oily wastewater. This water is often treated as waste instead of being cleaned and reused. Membrane filtration technologies, such as ultrafiltration (UF), offer a promising way to recover this water. However, oil fouling limits the effectiveness of these technologies. Oil droplets accumulate on the membrane surface and block water flow, which lowers treatment efficiency. Chemical demulsifiers are commonly used to treat oil–water mixtures, but their behavior during membrane filtration is not well understood. The mechanisms of demulsification at the membrane surface and how it affects oil fouling are not well understood. This CAREER project will study how chemical demulsifiers can be used in a strategic way to reduce fouling and improve membrane performance. By explaining how membrane demulsification works, the project will support development of more reliable and affordable methods to treat oily wastewater. The project will also broadens participation in environmental engineering through the Students for Environmental Engineering Development (SEED) program. The SEED program will engage K–12, undergraduate, and graduate students in hands-on learning about water sustainability and resource recovery. This CAREER project will elucidate the governing mechanisms of membrane demulsification and oil fouling mitigation in ultrafiltration (UF) systems for treating oily wastewater. The central hypothesis is that chemical demulsifiers, used either as membrane surface modifiers or as feedwater additives, regulate oil fouling by altering the kinetics of oil droplet coalescence and the interfacial interactions between oil droplets and membrane surfaces. The research will pursues three objectives: (1) to identify the mechanistic roles of oil droplet coalescence thermodynamics and kinetics and crossflow shear in governing membrane demulsification during UF processing; (2) to define structure–property–performance relationships for polymeric demulsifiers grafted onto membrane surfaces; and (3) to develop combination strategies of using reverse emulsion breakers as feed additives to further enhance membrane demulsification and mitigate oil fouling. A combination of crossflow UF experiments, Direct Observation Through the Membrane imaging, membrane fouling modeling, and extended DLVO theory will reveal oil droplet–membrane surface interactions and identify dominant fouling mechanisms. The project will incorporate data-driven modeling approaches (i.e., artificial intelligence (AI) tools) to identify relationships among operating conditions and membrane performance metrics. Quantum-based simulations using density functional theory (DFT) will be used to inform molecular-level interactions between demulsifier functional groups and model oil compounds, linking electronic structure to macroscopic fouling behavior. This project will generate new fundamental knowledge to advance membrane manufacturing and more effective strategies to control and mitigate membrane oil fouling in UF. The project team will develop lectures and hands-on experiments for high school, undergraduate, and graduate students to learn about the science and engineering of challenges around water sustainability in environmental engineering, including the treatment and reuse of oily wastewater. They will also include public engagement activities to further disseminate project findings with industry and utility stakeholders via the Industry Partnership Program of UTA’s Civil Engineering. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. NSF Award ID: 2542411 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Xiujuan Chen | Institution: University of Texas at Arlington, ARLINGTON, TX | Award Amount: $499,645 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2542411 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2542411.html