Cara Kilmartin

Chemical and Biomolecular Engineering

Faculty Advisor: William Phillip

Winter 2020-21 Project: Resource Recovery using Diafiltration Membrane Modeling and Separation Processes

The global water demand continues to stress supplies of freshwater, evoking a need to explore processes of producing usable water from nontraditional sources. Energy and water are intricately related, as reducing the energetic input of water purification processes is an essential component in developing sustainable methods to alleviate the global water burden. Membrane separations generally produce high purity water with less waste and relatively low energy demands. Desalination processes use reverse osmosis membranes to permeate water and reject all solutes, which generates potable water. However, reverse osmosis requires a large amount of energy to overcome the substantial osmotic pressure in separating a pure stream of water from a high salinity source. The energy intensity of reverse osmosis is a significant drawback to the technology, which has led to the exploration of hybrid processes that use less saline water sources, such as municipal and industrial wastewater. Hybrid membrane processes are generally less energy-intensive and have the potential for resource recovery while still producing a sufficient quality of water. Resource recovery is the process of using a waste source to extract something of value (e.g. nitrate and phosphorous can be recovered from human excreta). Nanofiltration is a pressure-driven membrane process that complements reverse osmosis. With pore sizes ranging from 2 – 8 nm in diameter, nanofiltration membranes exhibit selective rejection mechanisms rather than rejecting all solute indiscriminately. At this pore size, electrostatic interactions and van der Waal effects are principal for the membrane selectivity. For instance, the electrostatic repulsions between divalent cations and nanofiltration membranes result in these molecules being rejected at higher rates. However, smaller, monovalent salts can more easily permeate the membrane along with water, which helps to reduce the osmotic pressure and energy required. A parameter known as the sieving coefficient, which is calculated as the ratio of the solute concentration in the permeate solution to the solute concentration in the feed solution, describes the membrane selectivity for a given solute. The sieving coefficient is a function of the membrane pore size and solute sizes.

Electrostatic interactions between particles in nanofiltration can help mediate transport rates, which has important implications for resource recovery. By tailoring the electrostatic interactions and pore size, the nanofiltration process could target distinct solutes for recovery. The nanofiltration process alone becomes problematic, as typically more solvent is pushed across the membrane than solute, leading to drying out of solute. Diafiltration introduces an intermediary inflow of solvent to increase the amount of solute permeated across the membrane, making this process suitable for resource recovery. Targeting the recovery of resources (i.e. nitrate, phosphorus, and lithium) that exist in wastewater would increase sustainability and create a profit, which would reduce the cost associated with water reuse. The selectivity and permeability of diafiltration systems can be quantified through the use of a mathematical model. I derived the initial framework of the diafiltration model using a series of mass balances to predict the results of theoretical data. In order to predict the experimental results, I will adapt the model to fit the physical constraints of the diafiltration experiments conducted in the WATER lab. This improvement will allow for direct comparisons between predicted values and analytical data to ensure the model's accuracy. Two different nanofiltration membranes, which vary in sieving coefficients, will be analyzed for the solutes of interest and compared to the model's results to validate the model. Once validated, the model can be used to contemplate the trade-off between solute recovery and solute purity through an optimization process. The results of this optimization would directly inform future experiments with the hope of recovering an abundance of high purity resources while conserving resources, energy, and time.