Component innovations for lower cost mechanical vapor compression.

Despite significant capital and operating costs, mechanical vapor compression (MVC) remains the preferred technology for challenging brine concentration applications. This work seeks to assess the dependence of MVC costs on feedwater salinity and desired water recovery and to quantify the value of improved component performance or reduced component costs for reducing the levelized cost of water (LCOW) of MVC. We built a cost optimization model coupling thermophysical, heat and mass transfer, and technoeconomic models to optimize and identify low cost MVC system designs as a function of feedwater salinity and water recovery. The LCOW ranges over 3.6 to 6.1 $/m3 for seawater feed salinities of 25-150 g/kg and water recoveries of 40-80 %. We then perform sensitivity analysis on parameter inputs to isolate irreducible costs and determine high value component innovation targets. The LCOW was most sensitive to evaporator material costs and performance, including the overall heat transfer coefficient in the evaporator. Process and material innovations such as polymer-composite evaporator tubes that reduce evaporator costs by 25 % without reducing heat transfer performance by more than 10 % would result in MVC cost reductions of 8 %.

Modeling Framework for Cost Optimization of Process-Scale Desalination Systems with Mineral Scaling and Precipitation.

Cost-optimization models are powerful tools for evaluating emerging water treatment processes. However, to date, optimization models do not incorporate detailed chemical reaction phenomena, limiting the assessment of pretreatment and mineral scaling. Moreover, novel approaches for high-salinity and high-recovery desalination are typically proposed without direct quantification of pretreatment needs or mineral scaling. This work addresses a critical gap in the literature by presenting a modeling framework that includes complex water chemistry predictions with process-scale optimization. We use this approach to conduct a technoeconomic assessment on a conceptual high-recovery treatment train that includes chemical pretreatment (i.e., soda ash softening and recarbonation) and membrane-based desalination (i.e., standard and high-pressure reverse osmosis). We demonstrate how to develop and integrate accurate multidimensional surrogate models for predicting precipitation, pH, and mineral scaling tendencies. Our findings show that cost-optimal results balance the costs of pretreatment with reverse osmosis system design. Optimizing across a range of water recoveries (i.e., 50-90%) reveals multiple cost-optimal schemas that vary the chemical dosing in pretreatment and the design and operation of reverse osmosis. Our results reveal that pretreatment costs can be more than double the cost of the primary desalination process at high recoveries due to the extensive pretreatment required to control scaling. This work emphasizes the importance of and provides a framework for including chemistry and mineral scaling predictions in the evaluation of emerging technologies in high-recovery desalination.

High-impact innovations for high-salinity membrane desalination.

Reducing the cost of high-salinity (>75 g/L total dissolved solids) brine concentration technology would unlock the potential for vast inland water supplies and promote the safe management of concentrated aqueous waste streams. Impactful innovation will target component performance improvements and cost reductions that yield the highest impact on system costs, but the desalination community lacks methods for quantitatively evaluating the value of innovation or the robustness of technology platforms relative to competing technologies. This work proposes a suite of methods built on process-based cost optimization models that explicitly address the complexities of membrane-separation processes, namely that these processes comprise dozens of nonlinearly interacting components and that innovation can occur in more than one component at a time. We begin by demonstrating the merit of performing simple parametric sensitivity analysis on component performance and cost to guide the selection of materials and manufacturing methods that reduce system costs. A more rigorous implementation of this approach relates improvements in component performance to increases in component costs, helping to further discern high-impact innovation trajectories. The most advanced implementation includes a stochastic simulation of the value of innovation that accounts for both the expected impact of a component innovation on reducing system costs and the potential for improvements in other components. Finally, we apply these methods to identify innovations with the highest probability of substantially reducing the levelized cost of water from emerging membrane processes for high-salinity brine treatment.

Energy and CO2 Emissions Penalty Ranges for Geologic Carbon Storage Brine Management.

Safe and cost-effective geologic carbon storage will require active CO2 reservoir management, including brine extraction to minimize subsurface pressure accumulation. While past simulation and experimental efforts have estimated brine extraction volumes, carbon management policies must also assess the energy or emissions penalties of managing and disposing of this brine. We estimate energy and CO2 emission penalties of extracted brine management on a per tonne of CO2 stored basis by spatially integrating CO2 emissions from U.S. coal-fired electric generating units, CO2 storage reservoirs, and brine salinity data sets under several carbon and water management scenarios. We estimate a median energy penalty of 4.4-35 kWh/tonne CO2 stored, suggesting that brine management will be the largest post capture and compression energy sink in the carbon storage process. These estimates of energy demand for brine management are useful for evaluating end-uses for treated brine, assessing the cost of CO2 storage at the reservoir level, and optimizing national CO2 transport and storage infrastructure.

Cost Optimization of Osmotically Assisted Reverse Osmosis.

We develop a nonlinear optimization model to identify minimum cost designs for osmotically assisted reverse osmosis (OARO), a multistaged membrane-based process for desalinating high-salinity brines. The optimization model enables comprehensive evaluation of a complex process configuration and operational decision space that includes nonlinear process performance and implicit relationships among membrane stages, saline sweep cycles, and makeup, purge, and recycle streams. The objective function minimizes cost, rather than energy or capital expenditures, to accurately account for the trade-offs in capital and operational expenses inherent in multistaged membrane processes. Generally, we find that cost-optimal OARO processes minimize the number of stages, eliminate the use of saline makeup streams, purge from the first sweep cycle, and successively decrease stage membrane area and sweep flow rates. The optimal OARO configuration for treating feed salinities of 50-125 g/L total dissolved solids with water recoveries between 30-70% results in costs less than or equal to $6 per m3 of product water. Sensitivity analysis suggests that future research to minimize OARO costs should focus on minimizing the membrane structural parameter while maximizing the membrane burst pressure and reducing the membrane unit cost.