Nature Environment and Pollution Technology

T. R. Sethuraman ¹, K. V. Kavitha ¹

Affiliations
1 Sivaji College of Engineering and Technology, Manivilai-629 170, Distt. Kanyakumari, T.N., IN

Abstract

India produces huge volume of organic waste annually. This waste comes from agriculture, urban and industrial sources and also from domestic activities. Utilization of this waste material for productivity process is important for both economical and environmental reasons. In the present study an attempt was made to convert the biodegradable green wastes into value added compost using an exotic earthworm, Lumbricus rubellus. The experiments were carried out in a plastic tray at 50% concentrations of green waste for a period of 60 days. Chemical analysis of worked substrates indicated step-wise increase of nitrogen and phosphorus. The increase of phosphorus and nitrogen were found to be high for green waste undergoing vermicomposting. During the composting period the organic carbon decreased from its initial value 7.12% to 5.09%. The results indicate that 50% concentration of waste mixed with bedding material was ideal for the vermicomposting.

Keywords

Green Waste, Vermicomposting, Lumbricus rubellus.

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T. R. Sethuraman ¹, R. Murugesan ²

Affiliations
1 Department of Civil Engineering, Sun College of Engineering, Nagercoil-629 003, T.N., IN
2 Department of Civil Engineering, Institute of Road and Transport Technology, Erode, T.N., IN

Abstract

This paper serves as an introduction to artificial neural network ANN with emphasis on their application in Transportation Engineering. It presents a brief description of ANN, the underlaying concept and mathematical aspects and the role of ANN relative to other modelling approaches in transportation. Some popular ANN architecture and algorithms, and the merits and shortcomings of this methodology are discussed. The artificial neural network in this paper has demonstrated its usefulness and accuracy in predicting the traffic of Tuticorin port, which is going to gets maximum benefits due to the implementation of Sethu Samudram Ship Canal Project. The variation between the measured values and predicted values of traffic is very less, thus, proving that the artificial neural network has correctly predicted the traffic of Tuticorin port.

Keywords

Artificial Neural Network, Sethu Samudram Ship, Canal Project, Marine Traffic, Tuticorin Port.

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