The Limitations in Current Studies of Organic Fouling and Future Prospects

Nanoparticle-Embedded Polymers and Their Applications: A Review

There has been increasing interest in the study and development of nanoparticle-embedded polymeric materials and their applications to special membranes. Nanoparticle-embedded polymeric materials have been observed to have a desirable compatibility with commonly used membrane matrices, a wide range of functionalities, and tunable physicochemical properties. The development of nanoparticle-embedded polymeric materials has shown great potential to overcome the longstanding challenges faced by the membrane separation industry. One major challenge that has been a bottleneck to the progress and use of membranes is the balance between the selectivity and the permeability of the membranes. Recent developments in the fabrication of nanoparticle-embedded polymeric materials have focused on how to further tune the properties of the nanoparticles and membranes to improve the performance of the membranes even further. Techniques for improving the performance of nanoparticle-embedded membranes by exploiting their surface characteristics and internal pore and channel structures to a significant degree have been incorporated into the fabrication processes. Several fabrication techniques are discussed in this paper and used to produce both mixed-matrix membranes and homogenous nanoparticle-embedded polymeric materials. The discussed fabrication techniques include interfacial polymerization, self-assembly, surface coating, and phase inversion. With the current interest shown in the field of nanoparticle-embedded polymeric materials, it is expected that better-performing membranes will be developed soon.

SVM and ANN Modelling Approach for the Optimization of Membrane Permeability of a Membrane Rotating Biological Contactor for Wastewater Treatment

Membrane fouling significantly hinders the widespread application of membrane technology. In the current study, a support vector machine (SVM) and artificial neural networks (ANN) modelling approach was adopted to optimize the membrane permeability in a novel membrane rotating biological contactor (MRBC). The MRBC utilizes the disk rotation mechanism to generate a shear rate at the membrane surface to scour off the foulants. The effect of operational parameters (disk rotational speed, hydraulic retention time (HRT), and sludge retention time (SRT)) was studied on the membrane permeability. ANN and SVM are machine learning algorithms that aim to predict the model based on the trained data sets. The implementation and efficacy of machine learning and statistical approaches have been demonstrated through real-time experimental results. Feed-forward ANN with the back-propagation algorithm and SVN regression models for various kernel functions were trained to augment the membrane permeability. An overall comparison of predictive models for the test data sets reveals the model’s significance. ANN modelling with 13 hidden layers gives the highest R2 value of >0.99, and the SVM model with the Bayesian optimizer approach results in R2 values higher than 0.99. The MRBC is a promising substitute for traditional suspended growth processes, which aligns with the stipulations of ecological evolution and environmentally friendly treatment.

Novel Surrogates for Membrane Fouling and the Application of Support Vector Machine in Analyzing Fouling Mechanism

It is difficult to recognize specific fouling mechanisms due to the complexity of practical feed water, thus the current studies usually employ foulant surrogates to carry out research, such as alginate and xanthan gum. However, the representativeness of these surrogates is questionable. In this work, the classical surrogates (i.e., alginate and xanthan gum) were systematically studied, and results showed that they behaved differently during filtration. For the mixture of alginate and xanthan gum, both filtration behaviors and adsorption tests performed by quartz-crystal microbalance with dissipation monitoring (QCM-D) indicated that alginate plays a leading role in fouling development. Furthermore, by examining the filtration behaviors of extracellular polymeric substances (EPS) extracted from practical source water, it turns out that the gel layer formation is responsible for EPS fouling, and the properties of gel layer formed by EPS share more similarities with that formed from pectin instead of alginate. In addition, with the use of experimental data sets extracted from this study and our previous studies, a modeling method was established and tested by the support vector machine (SVM) to predict complex filtration behaviors. Results showed that the small differences of fouling mechanisms lying between alginate and pectin cannot be recognized by Hermia's models, and SVM can show a discrimination as high as 76.92%. As such, SVM may be a powerful tool to predict complex filtration behaviors.