Desalination

Manuscript title: Analysis of RED/dRED Stack Performance Using a Resistances in Series Model The worksheet includes raw data and processed from RED/dRED experiments. The data from the experiments are arranged in 18 tabs (please refer to Table 2 in the manuscript). Raw data includes current density, normalized voltages (multiple Pt wires and blank method), RIS model estimates and the inlet/outlet conductivities arranged column wise. For more details contact: Vishal Vipin Wagholikar email: vishal.w@u.nus.edu

Results from OLI Flowsheet modeling and DOW WAVE modeling of the baseline MVR and the advanced NR+RO+MVR designs

This dataset contains measurements from true batch RO experiments as well as the code use for data analysis. For more info, please see the accompanying paper.

Supporting Information

PS membranes were prepared from polymeric blends of PS and PMMA via a phase inversion induced by an immersion precipitation in water coagulation baths. The effects of the casting parameters (e.g., solvent selection, the composition of the coagulation bath) and the type of polystyrene (substituents at the aromatic ring, tacticity) on the morphology and water permeation flux were studied. The findings reveal that modified PS promoting instantaneous demixing with NMP/water systems result in membranes with macrovoids while the addition of 2% of PS-r-PMMA results in membranes with high water fluxes. The membranes morphologies were governed by a trade-off between thermodynamic and kinetic aspects. The antibacterial effect of (aPS)70-co-aPS(I)30/PMMA-based membrane was examined using static and cross-flow systems. These results illustrate the diversity in the design of these PS/PMMA based membranes and highlight the possibility to control the polymer chemistry for tailoring specific morphology, permeation and antibacterial properties for the desired function.

The interdisciplinary field between solar desalination and machine learning is the subject of a cutting-edge study. Generally, the studies treated data acquisition and model construction as independent processes, leading to problems such as insufficient dataset size or resource wastage. This study proposes a data-driven method that integrates data acquisition with model construction processes. By using the Bayesian optimization algorithm, the method accelerates the convergence of model accuracy. By comparing the results of 100 pairs of simulations, it is found that the models using the data-driven method are more accurate than traditional expert-driven methods in 70% of compared results. Additionally, when it makes a model with the mean absolute percentage error as 5%, the proposed data-driven method requires 220 additional data on average, compared to 258 with the traditional expert-driven method, representing a 14.7% reduction. This work offers new ways and a broad application of the interdiscipline between solar desalination and machine learning.

Meta data and all experimental data connected to Title: Water reuse and resource recovery from greenhouse wastewater by capacitive electrodialysis at pilot scale Authors: T. Guleria, J. van den Broeke, I. Platteau, T. Rijnaarts, A. Alhadidi, L. Gutierrez, E. Cornelissen Reference: Desal. 583 (2024) 117669

Selective extraction of target ions from aqueous solutions by capacitive deionization (CDI) has gradually become a highly efficient separation technology for water desalination. For the first time, a carbon coated olivine phase iron phosphate (O-FePO4@C) composite was developed by electrochemical delithiation of olivine phase LiFePO4@C composite as a pseudocapacitive electrode to explore the selective desalting behavior towards Na+, Mg2+, and Ca2+ ions. Significantly, this work also offered a new strategy for the recycling of waste lithium iron phosphate batteries, which is killing two birds with one stone. Batch experiments reflected both excellent electrosorption capacity (61.3 mg g-1 for Na+) at 1.2 V and good cycling stability of the O-FePO4@C electrode. Cyclic voltammetry (CV) and X-ray diffraction (XRD) combined with density functional theory (DFT) simulation revealed that the electrosorption mechanism of O-FePO4@C is derived from ion intercalation for Na+ and Mg2+, and electrical double layers (EDLs) adsorption for Ca2+, respectively, which exhibited outstandingly selective sodium(I) electrosorption ability. Combining density of state (DOS) of electrons with spectroscopic analysis revealed that the intercalated ions can impact electron configuration and improve the conductivity of the electrode material. The selective desalination performance of O-FePO4@C was also confirmed by treating natural groundwater samples.

This dataset contains data collected during experiment on foulant fractionation of seawater in reverse electrodialysis (RED). For explanation of the experimental setup we refer you to the published paper. It is being made public both to act as supplementary data for publication and in order for other researchers to use this data in their own work.

A dataset that contains experiment data from a hydrokinetic reverse osmosis pilot study. The study ran 11 experiments and collected observations every minute. To simulate the increase and decrease of pressure when non-continuous tidal energy is used to power the system, a sine wave is used to set the operating pressure between two values. The system is controlled by a “feed pump” and a “reject value” which generates pressure across the membrane. A link to a publication that resulted from this data and provides more details about the experimental conditions and results is included in the metadata for this dataset.