Nano-Iron Oxide Coating for Enhanced Heat Transfer in Gas–Solid Fluidized Bed Systems

This study explores using iron oxide coatings on glass beads to improve heat transfer efficiency in fluidized bed reactors. Techniques such as BET surface area analysis, SEM imaging, and X-ray diffraction were used to characterize the coated beads. Results showed the successful creation of a crystalline iron layer on the beads’ surface and increased thermal conductivity, especially at elevated temperatures. The study also quantified the impact of air surface velocity and heating power on the heat transfer coefficient, revealing substantial improvements, especially at higher velocities. It was found that the heat transfer coefficient for 600 µm glass beads increases significantly from 336.4 W/m2·K to 390.3 W/m2·K when the velocity is 0.27 m/s and the heating flux is 125 W. This demonstrates the effectiveness of the iron oxide coating in improving heat transfer. The results of this study emphasize the efficacy of iron oxide coatings in augmenting heat transmission characteristics, particularly in fluidized bed reactor

A comprehensive review on modelling the adsorption process for heavy metal removal from waste water using artificial neural network technique

Water is the most necessary and significant element for all life on earth. Unfortunately, the quality of the water resources is constantly declining as a result of population development, industry, and civilization progress. Due to their extreme toxicity, heavy metals removal from water has drawn researchers' attention. A lot of scientific applications use artificial neural networks (ANNs) because of their excellent ability to map nonlinear relationships. ANNs shown excellent modelling capabilities for the water treatment remediation. The adsorption process uses a variety of variables, making the interaction between them nonlinear. Selecting the best technique can produce excellent results; the adsorption approach for removing heavy metals is highly effective. Different studies show that the ANNs modelling approach can accurately forecast the adsorbed heavy metals and other contaminants in order to remove them