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Artificial Neural Network Modelling of Sequencing Batch Reactor Performance

Artificial Neural Network Modelling of Sequencing Batch Reactor Performance

Eldon R. Rene, Sung Joo Kim, Dae Hee Lee, Woo Bong Je, Mirian Estefanía López, Hung Suck Park
Copyright: © 2012 |Pages: 24
ISBN13: 9781613501160|ISBN10: 1613501161|EISBN13: 9781613501177
DOI: 10.4018/978-1-61350-116-0.ch019
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MLA

Rene, Eldon R., et al. "Artificial Neural Network Modelling of Sequencing Batch Reactor Performance." Handbook of Research on Computational Science and Engineering: Theory and Practice, edited by J. Leng and Wes Sharrock, IGI Global, 2012, pp. 456-479. https://doi.org/10.4018/978-1-61350-116-0.ch019

APA

Rene, E. R., Kim, S. J., Lee, D. H., Je, W. B., López, M. E., & Park, H. S. (2012). Artificial Neural Network Modelling of Sequencing Batch Reactor Performance. In J. Leng & W. Sharrock (Eds.), Handbook of Research on Computational Science and Engineering: Theory and Practice (pp. 456-479). IGI Global. https://doi.org/10.4018/978-1-61350-116-0.ch019

Chicago

Rene, Eldon R., et al. "Artificial Neural Network Modelling of Sequencing Batch Reactor Performance." In Handbook of Research on Computational Science and Engineering: Theory and Practice, edited by J. Leng and Wes Sharrock, 456-479. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-61350-116-0.ch019

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Abstract

Sequencing batch reactor (SBR) is a versatile, eco-friendly, and cost-saving process for the biological treatment of nutrient-rich wastewater, at varying loading rates. The performance of a laboratory-scale SBR was monitored to ascertain the chemical oxygen demand (COD) and total nitrogen (T-N) removals under four different operating conditions, by varying the operating time for the nitrification/denitrification steps, i.e., the cycle times. A multi-layered neural network was developed using COD, T-N, carbon to nitrogen ratio (C/N), aeration time, and mixed liquor suspended solids concentration (MLSS) data. This chapter compares the neural simulation results to the experimental results and extracts information on the significant factors affecting SBR performance. The application of artificial neural networks to biological processes such as SBR is a relatively new technique in wastewater and water quality management, and the results presented herein indicate the promising start of the adoption of computational science in this domain of research.

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