Utility of Column Lysimeter for Design of Soil Aquifer Treatment System for Wastewater Renovation Using Artificial Neural Networks
Publication: Journal of Environmental Engineering
Volume 130, Issue 12
Abstract
An artificial neural network (ANN) model is developed to study the correlation of data with reference to various wastewater pollution parameters (biochemical oxygen demand, chemical oxygen demand, suspended solids, , ) using two scales of experiments viz. column lysimeter and a pilot soil aquifer treatment (SAT) system for wastewater renovation in India. A unique feature of the study is that the primary treated wastewater was directly applied to SAT system for renovation in contrast to the secondary treated effluent used in most of the other studies that have been reported. The analysis of data using ANN as a tool indicates that the column lysimeter data are useful for design of SAT systems and it is possible to predict the effluent quality for SAT system based on the inputs from lysimeter experiments. The study highlights the utility of column lysimeter studies for evolving design parameters for a full-scale SAT system thereby obviating the need for pilot SAT studies which are site specific, time consuming, and expensive. Thus, the study suggests that the experimental data from lysimeter studies at a particular site can be used to predict performance of field-scale SAT systems without going in for actual experimentation. Further, the field data from one site could be utilized for design of SAT systems at other locations provided the climatic and hydrogeological conditions viz. soil matrix characteristics and wastewater characteristics, etc., are similar.
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Information & Authors
Information
Published In
Journal of Environmental Engineering
Volume 130 • Issue 12 • December 2004
Pages: 1534 - 1542
Copyright
Copyright © 2004 ASCE.
History
Published online: Nov 15, 2004
Published in print: Dec 2004
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