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Electroplating generates high volumes of rinse water that is contaminated with heavy metals. This study presents an approach for direct metal recovery and recycling from simulated rinse water, made up of an electroplating electrolyte used in industry, using reverse osmosis (RO). To simulate the real industrial application, the process was examined at various permeate fluxes, ranging from 3.75 to 30 L·m−2·h−1 and hydraulic pressures up to 80 bar. Although permeance decreased significantly with increasing water recovery, rejections of up to 93.8% for boric acid, >99.9% for chromium and 99.6% for sulfate were observed. The final RO retentate contained 8.40 g/L chromium and was directly used in Hull cell electroplating tests. It was possible to deposit cold-hued chromium layers under a wide range of relevant current densities, demonstrating the reusability of the concentrate of the rinsing water obtained by RO.
Porous polymer membranes substantially contribute to an acceleration of sustainability transformation based on the energy efficient separation of liquid and gaseous mixtures. This rapid shift toward sustainable industrial processes leads to an increased demand for specifically tailored membranes. In order to predict membrane performance factors like permeability, selectivity and durability, the membrane formation process by film casting and phase inversion needs to be understood further. In recent years, computational models of the membrane formation process have been studied intensely. Their high spatial and temporal resolution allows a detailed quantitative description of phase inversion phenomena. New experimental techniques complement this development, as they provide quantitative data, e.g., on compositional changes of the polymer solution during membrane formation as well as the kinetic progression of the phase separation process. This state-of-the-art review compiles computational and experimental approaches that characterize the phase inversion process. We discuss how this methodological pluralism is necessary for improving the tailoring of membrane parameters, but that it is unlikely to be the way to the ultimate goal of a complete description of the evolution of the membrane structure from the initial demixing to the final solidification. Alternatively, we formulate an approach that includes a database of standardized and harmonized membrane performance data based on previously publicized data, as well as the application of artificial neural networks as a new powerful tool to link membrane production parameters to membrane performance.