Effect of TiO on Thermal, Mechanical, and Gas Separation Performances of Polyetherimide-Polyvinyl Acetate Blend Membranes.

Blend membranes consisting of two polymer pairs improve gas separation, but compromise mechanical and thermal properties. To address this, incorporating titanium dioxide (TiO) nanoparticles has been suggested, to enhance interactions between polymer phases. Therefore, the objective of this study was to investigate the impact of TiO as a filler on the thermal, surface mechanical, as well as gas separation properties of blend membranes.

Blend membranes comprising polyetherimide and polyvinyl acetate with improved methane enrichment performance.

A clean and sustainable energy source, biogas is widely accessible worldwide. The caloric value of biogas is related to its methane content, and therefore removal of other gases is essential for reaping the benefits of this cleaner resource. In contrast to other classical techniques, membrane technology is relatively new yet extremely promising for methane enrichment.

Multiparameter Neural Network Modeling of Facilitated Transport Mixed Matrix Membranes for Carbon Dioxide Removal.

Membranes for carbon capture have improved significantly with various promoters such as amines and fillers that enhance their overall permeance and selectivity toward a certain particular gas. They require nominal energy input and can achieve bulk separations with lower capital investment. The results of an experiment-based membrane study can be suitably extended for techno-economic analysis and simulation studies, if its process parameters are interconnected to various membrane performance indicators such as permeance for different gases and their selectivity.

Multi-objective optimization of thermophysical properties of multiwalled carbon nanotubes based nanofluids.

The experimental determination of thermophysical properties of nanofluid (NF) is time-consuming and costly, leading to the use of soft computing methods such as response surface methodology (RSM) and artificial neural network (ANN) to estimate these properties. The present study involves modelling and optimization of thermal conductivity and viscosity of NF, which comprises multi-walled carbon nanotubes (MWCNTs) and thermal oil. The modelling is performed to predict the thermal conductivity and viscosity of NF by using Response Surface Methodology (RSM) and Artificial Neural Network (ANN).