The interest in metal sulfide precipitation has recently increased given its capacity to efficiently recover several metals and metalloids from different aqueous sources, including wastewaters and hydrometallurgical solutions. This article reviews recent studies about metal sulfide precipitation, considering that the most relevant review article on the topic was published in 2010. Thus, our review emphasizes and focuses on the overall process and its main unit operations. This study follows the flow diagram definition, discussing the recent progress in the application of this process on different aqueous matrices to recover/remove diverse metals/metalloids from them, in addition to kinetic reaction and reactor types, different sulfide sources, precipitate behavior, improvements in solid–liquid separation, and future perspectives. The features included in this review are: operational conditions in terms of pH and Eh to perform a selective recovery of different metals contained in an aqueous source, the aggregation/colloidal behavior of precipitates, new materials for controlling sulfide release, and novel solid–liquid separation processes based on membrane filtration. It is therefore relevant that the direct production of nanoparticles (Nps) from this method could potentially become a future research approach with important implications on unit operations, which could possibly expand to several applications.

In any membrane filtration, the prediction of permeate flux is critical to calculate the membrane surface required, which is an essential parameter for scaling-up, equipment sizing, and cost determination. For this reason, several models based on phenomenological or theoretical derivation (such as gel-polarization, osmotic pressure, resistance-in-series, and fouling models) and non-phenomenological models have been developed and widely used to describe the limiting phenomena as well as to predict the permeate flux. In general, the development of models or their modifications is done for a particular synthetic model solution and membrane system that shows a good capacity of prediction. However, in more complex matrices, such as fruit juices, those models might not have the same performance. In this context, the present work shows a review of different phenomenological and non-phenomenological models for permeate flux prediction in UF, and a comparison, between selected models, of the permeate flux predictive capacity. Selected models were tested with data from our previous work reported for three fruit juices (bergamot, kiwi, and pomegranate) processed in a cross-flow system for 10 h. The validation of each selected model’s capacity of prediction was performed through a robust statistical examination, including a residual analysis. The results obtained, within the statistically validated models, showed that phenomenological models present a high variability of prediction (values of R-square in the range of 75.91–99.78%), Mean Absolute Percentage Error (MAPE) in the range of 3.14–51.69, and Root Mean Square Error (RMSE) in the range of 0.22–2.01 among the investigated juices. The non-phenomenological models showed a great capacity to predict permeate flux with R-squares higher than 97% and lower MAPE (0.25–2.03) and RMSE (3.74–28.91). Even though the estimated parameters have no physical meaning and do not shed light into the fundamental mechanistic principles that govern these processes, these results suggest that non-phenomenological models are a useful tool from a practical point of view to predict the permeate flux, under defined operating conditions, in membrane separation processes. However, the phenomenological models are still a proper tool for scaling-up and for an understanding the UF process.

In the last decades, the incorporation of copper in polymeric membranes for water treatment has received greater attention, as an innovative potential solution against biofouling formation on membranes, as well as, by its ability to improve other relevant membrane properties. Copper has attractive characteristics: excellent antimicrobial activity, high natural abundance, low cost and the existence of multiple cost-effective synthesis routes for obtaining copper-based materials with tunable characteristics, which favor their incorporation into polymeric membranes. This study presents a comprehensive analysis of the progress made in the area regarding modified membranes for water treatment when incorporating copper. The notable use of copper materials (metallic and oxide nanoparticles, salts, composites, metal-polymer complexes, coordination polymers) for modifying microfiltration (MF), ultrafiltration (UF), nanofiltration (NF), forward osmosis (FO) and reverse osmosis (RO) membranes have been identified. Antibacterial and anti-fouling effect, hydrophilicity increase, improvements of the water flux, the rejection of compounds capacity and structural membrane parameters and the reduction of concentration polarization phenomena are some outstanding properties that improved. Moreover, the study acknowledges different membrane modification approaches to incorporate copper, such as, the incorporation during the membrane synthesis process (immobilization in polymer and phase inversion) or its surface modification using physical (coating, layer by layer assembly and electrospinning) and chemical (grafting, one-pot chelating, co-deposition and mussel-inspired PDA) surface modification techniques. Thus, the advantages and limitations of these modifications and their methods with insights towards a possible industrial applicability are presented. Furthermore, when copper was incorporated into membrane matrices, the study identified relevant detrimental consequences with potential to be solved, such as formation of defects, pore block, and nanoparticles agglomeration during their fabrication. Among others, the low modification stability, the uncontrolled copper ion releasing or leaching of incorporated copper material are also identified concerns. Thus, this article offers modification strategies that allow an effective copper incorporation on these polymeric membranes and solve these hinders. The article finishes with some claims about scaling up the implementation process, including long-term performance under real conditions, feasibility of production at large scale, and assessment of environmental impact.

The application of Machine Learning in Mineral Processing and Extractive Metallurgy has important benefits in terms of increasing the predictability and controllability of the processes, optimizing their performance, and improving maintenance. However, this application has significant implementation challenges. This paper analyzes these challenges and proposes ways of addressing them. Among the main identified challenges are data scarcity and the difficulty in characterizing abnormal events/conditions as well as modeling processes, which require the creative use of different learning paradigms as well as incorporating phenomenological models in the data analysis process, which can make the learning process more efficient. Other challenges are related to the need of developing reliable in-line sensors, adopting interoperability data models and tools, and implementing the continuous measurement of critical variables. Finally, the paper stresses the need for training of advanced human capital resources with the required skills to address these challenges.