Assessing wastewater reclamation potential by neural network model

Wastewater reclamation may produce many advantages including increased water conservation, better environmental protection, and attractive economic benefits. This will be deemed critical in south Taiwan where the heavy industries are concentrated but existing water resources are unable to meet the increasing demands for water supply. One particular wastewater reuse option is aquifer recharge using treated effluents that may alleviate land subsidence issue and seawater intrusion caused by unregulated aquaculture practices in the coastal region around this area. Yet wide variations of nutrient contents in treated effluents may make the implementation process of wastewater reclamation more sensitive. This paper presents a novel approach on the basis of a neural network model that is designed to provide better predictions of nitrogen contents in treated effluents to be used for groundwater recharge. The optimal structure of a neural network model useful for evaluating the reuse potential of the effluent produced from a contact-aeration process is developed. The importance of using oxidation/reduction potential readings as an input in the model to predict the degree of nitrification achieved is demonstrated. To enhance cost effectiveness in wastewater reclamation, a rainfall index is taken into account as a useful input in the model that provides the flexibility needed in decision-making process in accordance with the weather conditions observed. Research findings indicate that a well-trained neural network model is helpful in the assessment of wastewater reclamation practice to achieve significant cost savings with modest efforts in on-line sampling and analysis. (C) 2003 Elsevier Ltd. All rights reserved.