(739e) Mathematical Optimization of Membrane-Free Desalination Systems That Utilize Low-Grade Heat

Garciadiego, A., University of Notre Dame
Luo, T., University of Notre Dame
Dowling, A., University of Notre Dame
Mathematical Optimization of Membrane-free Desalination Systems that Utilize Low-grade Heat

Alejandro Garciadiego, Tengfei Luo, Alexander W. Dowling

Access to clean, fresh water is an ever-growing concern for modern society: water is critical to ensure human health, to protect threatened ecosystems, and to promote economic growth and prosperity. Modern seawater desalination technologies remain energy intensive; they required three to four times the theoretical minimum energy for separation[1]. This underscores the critical need for energy efficient and renewable driven desalination technologies to address these expanding needs for clean water.

Directional solvent extraction is a promising new desalination technology. It relies on liquid-liquid extraction with a thermoresponsive solvent that can be regenerated using low-grade heat. This approach has several unique features: (1) water can dissolve in the solvent, and the solubility is a function of temperature; (2) the solvent is virtually insoluble in water; (3) the solvent does not dissolve salts and 4) no membrane is required. Previous work includes initial concept demonstration as a batch process [2,3], molecular simulation to understand solvent performance [4], and heat integration for a single-stage continuous process [5].

In this work, we propose a mathematical optimization framework for the DSE process to explore trade-offs between product quality, energy requirements, and capital costs. This is done using an equation-oriented approach that simultaneously optimizes process operating conditions (such as temperatures, flow rates, compositions), design parameters (such as equipment sizes), and heat recovery opportunities [6,7]. The framework has the ultimate goal of systematically guiding molecular discovery of new thermoresponsive solvents. Results show that with the use of high-efficiency heat recovery DSE could be a competitive renewable membrane-free desalination technology.

Next, we consider sensitivity analysis and equipment costing to set physical property targets. We find two property targets are most important for DSE: i) the thermoresponsiveness of the solvent, i.e., the change in water solubility for a unit change in temperature, and ii) the solvent solubility in water.

Increasing the thermoresponsive from 0.0044 mol/mol / °C to 0.0022 mol/mol / °C decreases recycle ratio by from 80.2 kmole/s to 4 kmole/s. This decreases the heat and electricity costs from $2.20 per m3 of fresh water to $0.21 per m3 of fresh water, and equipment sizes by 400%. Lowering solubility of solvent in water decreases the solvent loss in the system and the costs associated with this. For decanoic acid, we calculate $3.30 per m3 of fresh water. Results also show that by doubling thermoresponsive ability and reducing the solubility of solvent in water by a factor of ten, can make DSE economical competitive with modern seawater desalination technologies. As ongoing work, we are exploring the viability of alternative classes of solvents, such as ionic liquids.


[1] M. Elimelech, W. A. Philip (2011). The Future of Seawater Desalination: Energy, Technology, and the Environment. Science 333 (6043), pp. 712-717.

[2] A. Bajpayee, T. Luo, A. Muto, G. Chen (2011). Very low temperature membrane-free desalination by directional solvent extraction. Energy Environ. Sci. 4, pp. 1672–1675.

[3] D. Rish, S. Luo, B. Kurtz, T. Luo (2014). Exceptional ion rejection ability of directional solvent for non-membrane desalination. Appl. Phys. Lett. 104 (2), p. 024102.

[4] T. Luo, A. Bajpayee, G. Chen (2011). Directional solvent for membrane-free water desalination—A molecular level study. J Appl. Physics., 110 (5), p. 054905.
[5] S. Alotaibi, O. M. Ibrahim, S. Luo, T. Luo (2017). Modeling of a continuous water desalination process using directional solvent extraction. Desalination 420, pp. 114-124.

[6] A.W. Dowling, L. T. Biegler (2015). A Framework for Efficient Large Scale Equation-Oriented Flowsheet Optimization. Comp. Chem. Eng. 72, pp. 3-20.

[7] A. W. Dowling, L. T. Biegler (2013). Optimization-based process synthesis for sustainable power generation. Chem. Eng. Trans. 35, pp. 1-12.