Forecasting Study for Nitrate Ion Removal Using Reactive Barriers
Cristina Modrogan, Ecaterina Diaconu, Oanamari Daniela Orbuleţ, Alexandra Raluca Miron · 2010
Levels of nitrates in groundwater in some instances are above the safe levels proposed by the EPA and thus pose a threat to human health. Passive groundwater remediation using permeable reactive barriers (PRBs) is a new and innovative technology for the removal of pollutants from groundwater. It acts as barrier against its contaminants, and removes them by adding an adsorption material for contaminants or a reactive material, able to interact with contaminants and diminish their bio-availability. In this paper, it was tried to find the best way to remove nitrate (NO 3 - ) ion from water using elemental iron according to water pH and water temperature. The results of the experiments were processed using an Artificial Neural Network (ANN) in order to create a mathematical model capable to predict the optimum quantity of iron needed to remove nitrate ion from the polluted water with nitrates in a given concentration. The present study analyses ANN as a mean to predict the nitrate ion removal from contaminated water. The parallel and distributed structure of artificial neural networks with their capabilities of generalization, fault tolerance, adaptive and associative performance, ability to perform dynamic and real-time functions, and their limited requirement of software, ensure their appropriateness for much practical environmental application.