Grantee Research Project Results

Developing Fixed-Bed Column Models for Novel Adsorbents to Predict the Removal of Emerging Micropollutants

EPA Grant Number: WT841175
Title: Developing Fixed-Bed Column Models for Novel Adsorbents to Predict the Removal of Emerging Micropollutants
Investigators: Shen, Yuexiao
Institution: Texas Tech University
EPA Project Officer: Chung, Serena
Project Period: November 1, 2025 through October 31, 2028
Project Amount: $1,000,000
RFA: Models to Predict the Removal of Emerging Micropollutants from Water by Novel Adsorbents in Fixed-BED Column Processes (2024) RFA Text
Research Category: Drinking Water , Water Quality , Water Treatment

Description:

The overarching goal of the proposed project is to address the challenges of promoting novel adsorbents to scalable column-based applications through three primary objectives. We have selected two types of novel adsorbents in our existing library, macrocyclic polymers and porous organic frameworks (POFs), which have shown exceptional uptake of pharmaceuticals and personal care products (PPCPs), endocrine disrupting chemicals (EDCs), per- and polyfluorinated substances (PFAS), and other micropollutants.

Objective:

Using these adsorbents, Objective 1 will address the challenge of the unusable sizes of these novel adsorbents (i.e., 1-50 μm) for column applications. They will be encapsulated in porous alginate beads or coated on porous silica to reach desirable sizes (i.e., 1-3 mm) without sacrifice of fast uptake kinetics and selectivity towards target micropollutants. Objective 2 will focus on developing a multi-scale modeling framework. We will evaluate an adsorption mechanistic model that explores mechanisms and equilibrium states at the microscopic level and an intra-particle diffusion model that examines adsorption kinetics, through a series of batch experiments. Objective 3 will combine outcomes of Objectives 1 and 2 into a column mass transport model via a series of rapid small-scale column tests (RSSCTs), the three components collectively represent the adsorption processes. We expect this mechanistic approach can follow ‘structure determines performance’ and provide predictions for which classes of novel adsorbents can most effectively remove specific classes of micropollutants. Lastly, Objective 4 is to validate resulting models using field column tests conducted at the Southern Nevada Water Authority’s drinking water and wastewater treatment plants. This team has extensive experience gained from on-going and previous projects regarding novel adsorbent development, modelling, and micropollutant characterization and is thus uniquely poised to achieve the proposed objectives. 

Approach:

(Task 1) We propose two facial granule synthesis approaches. One is incorporating functional adsorbents in highly permeable alginate beads; the other is coating adsorbents outside porous silica beads during in-situ synthesis. We hypothesize that such approaches can produce millimeter sized granular particles without sacrificing the exceptional adsorption properties of novel adsorbents (i.e., kinetics and maximum uptake capacity). (Task 2) A multi-scale modeling framework is proposed for novel adsorbents to remove micropollutants from the environment. (Task 3) Through a series of batch isotherm experiments, we are able to decouple the bulk solid-liquid adsorption coefficient in the adsorption equilibrium model into different components that reflect detailed mechanistic interactions between selected adsorbents and target micropollutants. Through a series of batch kinetic experiments using granular particles, we are able to determine the limiting step of an intra-particle diffusion model from the particle surface to internal adsorption sites via diffusion. (Task 4) When combining these two into a column mass transport model by a series of rapid small-scale column tests (RSSCTs), the three components collectively represent the adsorption processes. We are able to predict the column performances of new micropollutants based on existing physiochemical parameters, also predict the potential influence from competing adsorption such as natural organic matters (NOMs) and background ions. (Task 5) We expect current fixed-bed RSSCT results can scale up to large column test using dimensionless scaling factor, but with computational fluid modeling and experimental trace studies, we can minimize prediction errors. 

Expected Results:

1) A multi-scale framework model developed to evaluate the effectiveness of novel adsorbents to remove target micropollutant from aqueous phases. The framework will show the mechanistic interactions between adsorbents and micropollutants. It will also predict the performance of these adsorbents when synthesized into granules and packed in full-scale, fixed-bed, flow-through units. 2) Mechanistic interaction parameter database for modeling from batch experiments that can be expanded in the future studies for model prediction. 3) Novel adsorbents as granules applicable for column applications. This will solve the challenge of unusable powder size of most newly invented adsorbents for fix-bed column filtration applications. 4) Optimization of the scaling factor that bridges the gap between RSSCT and pilot to full-scale column applications using numerical simulation of the hydraulic conditions.

Supplemental Keywords:

fixed-bed column filtration, mechanistic, micropollutant, modeling, novel adsorbent