Publications by authors named "Shirsendu Banerjee"
Article Synopsis
- The study evaluates how well different regression methods estimate the performance of TiO/Cu catalysts for converting CO to methanol, specifically using hydrogen exfoliated graphene (HEG).
- The research found that 30 wt% HEG provided the best conversion efficiency in producing methanol, with key factors such as HEG dosing and CO inflow rate significantly influencing the results.
- Nonlinear regression via artificial neural networks (ANN) outperformed linear regression methods, achieving a higher determination coefficient and more accurate predictions in line with experimental data.
The present study deals with two-phase non-Newtonian pseudoplastic crude oil and water flow inside horizontal pipes simulated by ANSYS. The study helps predict velocity and velocity profiles, as well as pressure drop during two-phase crude-oil-water flow, without complex calculations. Computational fluid dynamics (CFD) analysis will be very important in reducing the experimental cost and the effort of data acquisition.
View Article and Find Full Text PDFThe present study deals with sonochemically in situ synthesis of a novel functional catalyst using hydrogen exfoliated graphene (HEG) supported titanium dioxide (TiO) and copper sulfate (CuSO) doped with zinc oxide (ZnO) (abbreviated as Ti/Cu/Zn-HEG). The synthesis of the Ti/Cu/Zn-HEG nanocomposite (NCs) catalyst was confirmed through its characterizations by XRD, SEM-EDX, TEM, XPS, FTIR, and BET methods. It was assessed for catalytic conversion of a model aromatic compound -nitrophenol (p-NP) in an aqueous solution.
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