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: Control theory & Electric power system. The organization has 1934 authors who have published 4219 publications receiving 41149 citations. The organization is also known as: NIT Silchar.

A Non-Conventional Adsorbent for the Removal of Clofibric Acid from Aqueous Phase

Abstract: The adsorption of Clofibric acid, one of the most frequently prescribed high environmental risk drugs, was studied using H3PO4 activated Schumannianthus dichotomus (ASD). The chemical characteristics of the adsorbent were established by Bohem’s titration, pHPZC, FTIR, SEM, XRD, porosity, and surface area analysis. It was observed that the adsorbent was microporous-mesoporous in nature with BET surface area of 1199.98 m2.g−1. The influence of temperature (303-323 K), pH (2-10), textural properties, adsorbent load, and contact time was studied. The Langmuir equation was found to best represent the equilibrium data for clofibric acid-adsorbent system, yielding monolayer adsorption capacity of 258.39 mg.g−1 at 303 K. The pseudo-second order model best explained (R2 > 0.999) the adsorption kinetics with rate constant 0.037 g.mg−1min−1. The thermodynamics parameters, ΔG°, ΔH°, and ΔS°, evaluated as −8.14 kJmol−1, −34.07 kJmol−1, and −85.5 JK−1mol−1, respectively, revealed that the adsorption process is feasible...

Detection and classification of power quality disturbances using wavelet transform, fuzzy logic and neural network

Abstract: This paper presents an approach for detection and classification of power quality disturbances using wavelet transform, fuzzy logic and neural network. The total harmonic distortion (THD) and energy of the disturb signals are used for classification. A maiden attempt is made to apply a new tool called neuro solution for artificial neural network (ANN) in the field of power quality disturbance classification. A comparison of fuzzy logic and neural network for disturbance classification has been made. Comparison of these two techniques reveals that ANN is more accurate and efficient than the fuzzy logic.

Seismic damage assessment and prediction using artificial neural network of RC building considering irregularities

Abstract: This paper investigated multi-objective seismic damage assessment procedure. Primarily, it estimates damage index (DI) of reinforcement concrete (RC) framed low-rise residential buildings under the...