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Hindi Research Article
Assessing Bioremediation of Acid Mine Drainage in Coal Mining Sites Using a Predictive Neural Network-Based Decision Support System NNDSS)
| Xiaoci Ji1, Steven A Ripp2, Alice C Layton2, Gary S Sayler2,3, Jennifer M DeBruyn1* | |
| 1Department of Biosystems Engineering and Soil Science, The University of Tennessee, USA | |
| 2Center for Environmental Biotechnology, The University of Tennessee, USA | |
| 3Department of Microbiology, The University of Tennessee, USA | |
| Corresponding Author : | Jennifer M DeBruyn Department of Biosystems Engineering and Soil Science The University of Tennessee, USA Tel: 865-974-7266 E-mail: jdebruyn@utk.edu |
| Received September 05, 2013; Accepted October 04, 2013; Published October 10, 2013 | |
| Citation: Ji X, Ripp SA, Layton AC, Sayler GS, DeBruyn JM (2013) Assessing Long Term Effects of Bioremediation: Soil Bacterial Communities 14 Years after Polycyclic Aromatic Hydrocarbon Contamination and Introduction of a Genetically Engineered Microorganism. J Bioremed Biodeg 4:209. doi: 10.4172/2155-6199.1000148 | |
| Copyright: © 2013 Ji X, et al. This is an open-a ccess article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. | |
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Abstract
In this study, an Artificial Neural Network (ANN) was developed as a predictive tool for identifying optimal remediation conditions for groundwater contaminants that include selected metals found at coal mining sites. The ANN was developed from a previous field data obtained from a bioremediation project at an abandoned mine at Cane Creek in Alabama, and from a coal pile run off at a Department of Energy’s site in Aiken, South Carolina. The evaluative parameters included pH, redox, nutrients, bacterial strain (MRS-1), and type of microbial growth process (aerobic, anaerobic or sequential aerobic-anaerobic conditions). Using the conditions predicted by the Neural Networks, significant levels of As, Pb, and Se were precipitated and removed over eight days in remediation assays containing 10 mg/L of each metal in cultures that include MRS-1. The results showed 85%, 100%, and 87% reductions of As, Pb, and Se, respectively. The results from these ANN- driven assays are significant. It provides a roadmap for reducing the technical risks and uncertainties in clean-up programs. Continuous success in these efforts will require a strong and responsive research that provides a decision support system for long-term restoration efforts.
