Kongu Engineering College

About: Kongu Engineering College is a based out in . It is known for research contribution in the topics: Computer science & Cluster analysis. The organization has 2001 authors who have published 1978 publications receiving 16923 citations.

Effects of Microwave Annealing of Graphene and its Impact on Structural, Electrochemical Performance for Energy Storage

Abstract: In this paper, graphene was prepared via modified hummers’ method and then with the help of microwave treatment of as prepared graphene, microwave graphene (MG) was prepared. From the electrochemical experiments, graphene and MG exhibited improved electrochemical performance. Electrochemical performance of graphene and MG electrodes were measured by cyclic voltammetry (CV), galvanostatic charge and discharge measurement (GCD) and electrochemical impedance spectroscopy (EIS). The specific capacitance value of 275 Fg-1 And 316 Fg-1 for graphene and MG, respectively. Equivalent series resistance of MG was 1.78 Ω and charge transfer resistance was 1.33 Ω, smaller than that of graphene. The fabricated electrodes of supercapacitor show the improved equivalent series resistance and capacitance value. The improved performance of MG electrode was ascribed to the porous nature of graphene after microwave annealing. This work gives the great interest towards the material for high performance high energy and power density application.

Recycling of Woven Fabric Dyeing Wastewater Practiced in Perundurai Common Effluent Treatment Plant

Abstract: Textile dyeing industries in Erode and Tirupur district of Tamilnadu (India) discharge effluents ranging between 100 and 200m³/t of production. Dyeing is performed by Jigger or advanced Soft Flow reactor process. Coloring of hosiery fabric takes place in the presence of high concentration of sodium sulphate or sodium chloride (30 – 75 kg/m³) in dye solutions. Wash water and dye bath waste water are the process effluents of dyeing industry which are collected separately and follow the advanced treatment for maximum recycling of recovered waters.Wash water is treated using a sequence of physicochemical and biological unit process, the waste water is passed into ultrafiltration (UF), two stages reverse osmosis (RO) membrane system where the permeate is reused for processes. The rejects about 10 – 12 % of the inlet volume is subject to reverse osmosis for sent to evaporators. Dye bath water after treating, the permeate is used in process for dye bath preparation and the reject of about 20 – 25% is sent to multi effect evaporator / solar evaporation pond (SEP). The final rejects from reverse osmosis system is directed to multi effect evaporator system where condensed waters are recovered. The removal of Total Dissolved Solids (TDS), Chemical Oxygen Demand (COD) and Chloride are in the range of 82 – 97%, 90 – 97% and 78 – 97% respectively. This study was carrier out Common Effluent Treatment Plant (CETP), Perundurai, SIPCOT, Erode district.

Weed Identification using Convolutional Neural Network and Convolutional Neural Network Architectures

Abstract: In order to overcome this threat imposed by weeds in agriculture, a measure is taken to identify the weeds that grow along with the seedlings with the help of deep learning (DL) technique. Convolutional neural network (CNN), a class of DL render a good way to identify the weeds that harm the plant’s growth. Aiming at achieving a greater accuracy, the models such as four convolution layered, six convolution layered, eight convolution layered and thirteen convolution layered architecture were built. Comparatively, eight convolution layered architecture resulted with 97.83% as training accuracy and 96.53% of validation accuracy than the VGG-16 model resulted with. The use of CNN architectures paved way to reach training accuracy of 96.27% and validation accuracy with 91.67% in ZFNet and 97.63% as training accuracy and 92.62% of validation accuracy in ALEXNET. Therefore, by the use of this technology and suggested method there is a lot of possibilities to avoid the manual field work of identifying the weeds. Our results suggest that more of datasets can be used and fine-tuning of parameters can be done.