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Received April 17, 2012
Accepted August 5, 2012
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This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits
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Neural network prediction of fluidized bed bioreactor performance for sulfide oxidation
Department of Chemical Engineering, National Institute of Technology, Jalandhar 144 011, India 1Department of Biotechnology, National Institute of Technology, Durgapur, India
midhav@rediffmail.com
Korean Journal of Chemical Engineering, February 2013, 30(2), 385-391(7), 10.1007/s11814-012-0128-7
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Abstract
Sulfide oxidation rate of a fluidized bed bioreactor was predicted using ANN, with upflow velocity, hydraulic retention time, reactor operation time and pH given as input. The reactor was fed with 100mg/L synthetic sulfide wastewater after biofilm formation on nylon support particles. Feedforward neural network model was prepared using 81 data sets, of which 63 were used for training and 18 for testing in a three-way cross validation. Prediction performance of the network was evaluated by calculating the percent error of each data set and mean square error for test data set in three partitions. The mean square error for test data set was 5.55, 4.08 and 2.30 for partition 1, partition 2 and partition 3, respectively. The predicted sulfide oxidation values correlated with the experimental values and a correlation coefficient of 0.96, 0.97 and 0.98 was obtained for partition 1, partition 2 and partition 3, respectively.
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References
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Vinod AV, Kumar KA, Reddy GV, Biochem. Eng. J., 46, 12 (2009)
Wang AJ, Liu CS, Han HJ, Ren NQ, Lee DJ, J. Hazard. Mater., 168(2-3), 1274 (2009)
Delnavaz M, Ayati B, Ganjidoust H, J. Hazard. Mater., 179(1-3), 769 (2010)
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Sreekrishnan TR, Ramachandran KB, Ghosh P, Biotechnol.Bioeng., 37, 557 (1991)
Midha V, Jha MK, Dey A, J. Eng. Sci. Technol., Inpress.
Maier HR, Dandy GC, Environ. Modell. Softw., 13, 179 (1998)
Nguyen D, Widrow B, Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights, In: Proceedings of the International Joint Conference on Neural Networks, 3, 21 (1990)
Abdi H, Valentin D, Edelman B, O’Toole AJ, J. Math., 40, 175 (1996)
El-Din AG, Smith DW, Water Res., 36, 1115 (2002)
Toth E, Brath A, Montanari A, J. Hydrol., 239, 132 (2000)
Guha A, Application of artificial neural network for predicting yarn properties and process parameters, Ph.D Thesis, Indian Institute of Technology, Delhi, India (2002)
Midha V, Jha MK, Dey A, J. Environ. Sci., 24, 513 (2012)
