Potential of nanofiltration technology in recirculating aquaculture systems in a context of circular economy

Micropollutants removal from aquaculture water using layer-by-layer self-assembled nanofiltration membranes

Micropollutants (MPs) in aquaculture water are directly related to human health, but largely overlooked. The conventional water treatment technologies could not effectively remove MPs, and new technologies have been pursued with high MP removal rate, low cost and small footprint. This paper reported the first endeavor of using layer-by-layer (LBL) hollow fiber (HF) nanofiltration (NF) membranes to treat real aquaculture water. We compared three types of membranes with various surface chemical properties and pore sizes assembled by different polyelectrolytes (PEs). The MPs removal rates and operation stability of three NF membranes were studied in flocculation (Floc.)/ultrafiltration (UF)/NF, Floc./NF and direct NF processes. The best poly(styrene sulfonate)/poly(allylamine hydrochloride) (PSS/PAH) NF membrane showed almost 100 % MPs removal in Floc./UF/NF, and above 95 % in direct NF process. Stable permeance was observed in three 24 h cycles for three processes. The correlation hot spot analysis between the physical properties and removal rates of MPs confirmed importance of size exclusion as the important factor for removal of MPs. The organic humus, rather than inorganic silicon and calcium, was found as the main foulant, which increased slightly the pore size of the LBL membranes with small pores, but blocked pores of membranes with large pores, affected the rejection for ions as well. The cost assessment based on (PSS/PAH)2.5 showed a total cost below 0.75 ¥/ton water, which is feasible to efficiently remove MPs from aquaculture water by NF membrane process.

Zeolite Imidazolate Frameworks-8@SiO2-ZrO2 Crystal-Amorphous Hybrid Core-Shell Structure as a Building Block for Water Purification Membranes

Metal-organic frameworks (MOFs) are emerging as promising materials for water purification membranes, owing to their uniform microporous structures and chemical functionalities. Here, we report a simple procedure for depositing MOF-based nanofiltration membranes on commercial TiO2 ceramic tubular supports, completely avoiding the use of dispersants or binders. Zeolite imidazolate frameworks-8 (ZIF-8) nanocrystals were synthesized in methanol at room temperature and subsequently coated with an amorphous SiO2-ZrO2 gel to generate a dispersion of ZIF-8@SiO2-ZrO2 core-shell nanoparticles. The amorphous SiO2-ZrO2 gel served as a binding agent for the ZIF-8 nanocrystals, thus forming a defect-free continuous membrane layer. After repeating the coating twice, the active layer had a thickness of 0.96 μm, presenting a rejection rate >90% for the total organic carbon in an aquaculture effluent and in a wastewater treatment plant, while reducing the concentration of trimethoprim, here used as a target pollutant. Moreover, the oxide gel provided the MOF-based active layer with good adhesion to the support and enhanced its hydrophilicity, resulting in a membrane with excellent mechanical stability and resistance to fouling during the crossflow filtration of the real wastewater samples. These results implied the high potential of the MOF-based nanocomposite membrane for effective treatment of actual wastewater streams.

Modeling, optimization and understanding of adsorption process for pollutant removal via machine learning: Recent progress and future perspectives

It is crucial to reduce the concentration of pollutants in water environment to below safe levels. Some cost-effective pollutant removal technologies have been developed, among which adsorption technology is considered as a promising solution. However, the batch experiments and adsorption isotherms widely employed at present are inefficient and time-consuming to some extent, which limits the development of adsorption technology. As a new research paradigm, machine learning (ML) is expected to innovate traditional adsorption models. This reviews summarized the general workflow of ML and commonly employed ML algorithms for pollutant adsorption. Then, the latest progress of ML for pollutant adsorption was reviewed from the perspective of all-round regulation of adsorption process, including adsorption efficiency, operating conditions and adsorption mechanism. General guidelines of ML for pollutant adsorption were presented. Finally, the existing problems and future perspectives of ML for pollutant adsorption were put forward. We highly expect that this review will promote the application of ML in pollutant adsorption and improve the interpretability of ML.