In the design of membrane distillation systems, the effect of different heat transfer coefficient models on the transmembrane flux seems to have been overlooked thus far. Interestingly, the range of discrepancy in the results of the transmembrane flux is wide, especially in the laminar flow region, where MD is often operated. This can be inferred by studying the design and parameters of the direct contact membrane distillation system. In this study, the physical and physiochemical properties that affect the design of MD are comprehensively reviewed, and based on the reviewed parameters, an MD design algorithm is developed. In addition, a cost analysis of the designed MD process for low-grade-energy fluids is conducted. As a result, a total unit product cost of USD 1.59/m3, 2.69/m3, and 15.36/m3 are obtained for the feed velocities of 0.25, 1 and 2.5 m/s, respectively. Among the design parameters, the membrane thickness and velocity are found to be the most influential.
In this study, a framework for the
prediction of thermophysical
properties based on transfer learning from existing estimation models
is explored. The predictive capabilities of conventional group-contribution
methods and traditional machine-learning approaches rely heavily on
the availability of experimental datasets and their uncertainty. Through
the use of a pretraining scheme, which leverages the knowledge established
by other estimation methods, improved prediction models for thermophysical
properties can be obtained after fine-tuning networks with more accurate
experimental data. As our experiments show, for the case of critical
properties of compounds, this pipeline not only improves the performance
of the models on commonly found organic structures but can also help
these models generalize to less explored areas of chemical space,
where experimental data is scarce, such as inorganics and heavier
organic compounds. Transfer learning from estimation models data also
allows for graph-based deep learning models to create more flexible
molecular features over a bigger chemical space, which leads to improved
predictive capabilities and can give insights into the relationship
between molecular structures and thermophysical properties. The generated
molecular features can discriminate behavior discrepancy between isomers
without the need of additional parameters. Also, this approach shows
better robustness to outliers in experimental datasets.
Groundwater salinization is a problem affecting access to water in many world regions. Though desalination by conventional reverse osmosis (RO) can upgrade groundwater quality for drinking, its disadvantages include unmanaged brine discharge and accelerated groundwater depletion. Here, we propose a new approach combining RO, forward osmosis (FO), and halophyte cultivation, in which FO optimally adjusts the concentration of the RO reject brine for irrigation of Salicornia or Sarcocornia. The FO also reuses wastewater, thus, reducing groundwater extraction and the wastewater effluent volume. To suit different groundwater salinities in the range 1−8 g/L, three practical designs are proposed and analyzed. Results include specific groundwater consumption (SGC), specific energy consumption (SEC), wastewater volume reduction, peak RO pressure, permeate water quality, efficiency of water resource utilization, and halophyte yield. Compared to conventional brackish water RO, the results show superior performance in almost all aspects. For example, SGC is reduced from 1.25 to 0.9 m 3 per m 3 of drinking water output and SEC is reduced from 0.79 to 0.70 kW h/m 3 by a FO−RO−FO system treating groundwater of salinity 8 g/L. This system can produce 1.1 m 3 of high-quality drinking water and up to 4.9 kg of edible halophyte per m 3 of groundwater withdrawn.
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