National Institute of Technology, Silchar

About: National Institute of Technology, Silchar is a education organization based out in Silchar, Assam, India. It is known for research contribution in the topics: Computer science & Control theory. The organization has 1934 authors who have published 4219 publications receiving 41149 citations. The organization is also known as: NIT Silchar.

Adsorption of malachite green in aqueous solution onto sodium carbonate treated rice husk

Abstract: The feasibility of using rice husk for removing malachite green (MG) from aqueous solutions has been investigated as a low cost and an eco-friendly adsorbent. The effect of chemical treatment of rice husk by sodium carbonate was investigated through SEM and FTIR. As well, the effect of various factors like rotational speed, pH value of solution, temperature, initial concentration of dye, dose of adsorbent, contact time on the adsorption of MG was examined in batch experiments. Adsorption isotherms, kinetics, mass transfer and rate limiting steps are studied in detail. Adsorption thermodynamic parameters, activation energy and isosteric heat of adsorption were also studied. The adsorption was a spontaneous, endothermic and followed the pseudo-second-order kinetic model. The mechanism of adsorption and the COD aspect in deciding the effectiveness of the biomaterial as an adsorbent for dyes has also to be taken into account.

A Survey on Pareto-Based EAs to Solve Multi-objective Optimization Problems.

Abstract: Most of the real-world optimization problems have multiple objectives to deal with. Satisfying one objective at a time may lead to the huge deviation in other. Therefore, an efficient tool is required which can handle multiple objectives simultaneously in order to provide a set of desired solutions. In view of this, multi-objective optimization (MOO) attracts the attention of the researchers since last few decades. Many classical optimization techniques have been proposed by the researchers to solve the multi-objective optimization problems. However mostly, the gradient-based approaches fail to handle complex MOO problems. Hence, as an alternative, researchers have shown their interest toward population-based optimization approaches to solve the MOO problems and come up with convincing results even in the complex environment. Evolutionary algorithms (EAs), which are the first in the group of population-based approach, enjoy almost a decade in providing the solutions to MOO problems. The real challenge is to achieve the set of solutions called Pareto-optimal set. The smooth landing on such set is only possible if there exists diversified solution in the population. Due to the continuous effort, there is a gradual development in the proposition of various efficient Pareto-based approaches in the literature to solve MOEAs. A critical review of those approaches is being carried out in this present study.