Assessing Bioremediation of Acid Mine Drainage in Coal Mining Sites Using a Predictive Neural Network-Based Decision Support System NNDSS) | OMICS International | Abstract
ISSN: 2155-6199

Journal of Bioremediation & Biodegradation
Open Access

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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: [email protected]
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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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.