Indian Institute of Technology Bhubaneswar

About: Indian Institute of Technology Bhubaneswar is a education organization based out in Bhubaneswar, India. It is known for research contribution in the topics: Large Hadron Collider & Higgs boson. The organization has 1185 authors who have published 3132 publications receiving 48832 citations.

Effective utilization of a sponge iron industry by-product for phosphate removal from aqueous solution: A statistical and kinetic modelling approach

Abstract: Dolochar, a solid waste generated from sponge iron industry during the process of direct reduction of iron by rotary kiln, is explored as an adsorbent for phosphate removal in this study. The depiction of the adsorption process was done by X-ray diffraction (XRD), Fourier transferred infra-red spectroscopy (FTIR), and Energy dispersive spectroscopy (EDS) analysis. The appearance of phosphorous peak in EDS spectra of spent dolochar confirmed phosphate adsorption. Application of response surface methodology (RSM) and analysis of variance (ANOVA) for modelling and optimization of phosphate removal in batch study and breakthrough time in column study, suggested quadratic models for both the responses. Experimental validation of the optimization process resulted in 98.13% phosphate removal and 24.67 h breakthrough time. Pseudo second order kinetic and Langmuir isotherm illustrated best fit to the experimental data with R2 = 0.98 and R2 = 0.99, respectively. The values of separation factor (1 > RL > 0), Freundlich exponent (n > 1) and thermodynamic parameters (ΔG°, −3442.6 kJ/mol and ΔH°, 6627 kJ/mol) specified favourable spontaneous and endothermic adsorption process. The adsorbent displayed 80% of the original adsorption capacity in the 3rd cycle of reuse. The results of this study support the utility of dolochar as a low cost and highly efficient adsorbent for phosphate removal from aqueous solution.

Robust identification of nonlinear complex systems using low complexity ANN and particle swarm optimization technique

Abstract: The paper introduces a novel method of adaptive robust identification of complex nonlinear dynamic plants including Box Jenkin, Mackey Glass and Sunspot series under the presence of strong outliers in the training samples. The identification model consists of a low complexity single layer functional link artificial neural network (FLANN) in the feed forward path and another on the feedback path. The connecting weights are iteratively adjusted by a population based particle swarm optimization technique so that a robust cost function (RCF) of the model-error is minimized. To demonstrate robust identification performance up to 50% random samples of the plant output is contaminated with strong outliers and are employed for training the model using PSO tool. Identification of wide varieties of benchmark complex static and dynamic plants is carried out through simulation study and the performance obtained are compared with those obtained from using standard squared error norm as CF. It is in general observed that, the Wilcoxon norm provides best identification performance compared to squared error and other RCFs based models.

Field emission properties of ZnO nanosheet arrays

Abstract: Electron emission properties of electrodeposited ZnO nanosheet arrays grown on Indium tin oxide coated glass substrates have been studied. Influence of oxygen vacancies on electronic structures and field emission properties of ZnO nanosheets are investigated using density functional theory. The oxygen vacancies produce unshared d electrons which form an impurity energy state; this causes shifting of Fermi level towards the vacuum, and so the barrier energy for electron extraction reduces. The ZnO nanosheet arrays exhibit a low turn-on field of 2.4 V/μm at 0.1 μA/cm2 and current density of 50.1 μA/cm2 at an applied field of 6.4 V/μm with field enhancement factor, β = 5812 and good field emission current stability. The nanosheet arrays grown by a facile electrodeposition process have great potential as robust high performance vertical structure electron emitters for future flat panel displays and vacuum electronic device applications.

Evaluation of PM2.5 Surface Concentrations Simulated by NASA’s MERRA Version 2 Aerosol Reanalysis over India and its Relation to the Air Quality Index

Abstract: The PM2.5 (particulate matter with a diameter ≤ 2.5 µm), an essential component of air pollution, is closely linked to adverse effects on human health, including premature mortality following prolonged exposure. However, limited surface measurement and the lack of monitoring with adequate spatial resolution hamper studies related to air pollution and its impact on various societally relevant issues. More recently, the National Aeronautics and Space Administration (NASA)’s Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) has begun estimating the global distribution of PM2.5 mass concentrations at high spatio-temporal resolutions, but the limitations of the applied estimation methodologies must be carefully evaluated in order to understand their strengths and weaknesses. This study assesses MERRA-2’s PM2.5 results by comparing them with ground-based measurements conducted at 20 stations across the Indian region between 2015 and early 2018. Our analysis shows that MERRA-2 generally underestimates the PM2.5 in terms of both the mass concentration and the number of exceedance days. While the Central Pollution Control Board (CPCB) measured exceedances of the national ambient air quality standards (NAAQS) on 34% of the days, MERRA-2’s prediction was only 11%, and its estimate of the annual average PM2.5 concentration across all of the sites was also negatively biased, by ~27 µg m–3. Correlations of 0.96 and 0.6 were found between the estimates and the measurements for the monthly and the daily averaged concentrations, respectively; these numbers can be dramatically improved by applying a simple bias correction. Overall, our evaluation reveals that MERRA-2’s raw estimates of PM2.5 on a monthly time scale or longer are helpful in long-term air quality studies.