Neural network prediction of fluidized bed bioreactor performance for sulfide oxidation

Varsha Midha; Mithilesh Kumar Jha; Apurba Dey

Korean Journal of Chemical Engineering, Vol.30, No.2, 385-391, February, 2013

DOI10.1007/s11814-012-0128-7 Export Citation

Neural network prediction of fluidized bed bioreactor performance for sulfide oxidation

Varsha Midha^†, Mithilesh Kumar Jha, and Apurba Dey¹

Department of Chemical Engineering, National Institute of Technology, Jalandhar 144 011, India
¹Department of Biotechnology, National Institute of Technology, Durgapur, India

E-mail:

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.

Keywords:FBBR;Mean Square Error;Neural Network;Sulfide;Sulfur

[References]

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Kuenen JG, Plant Soil., 43, 49 (1975)
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Fox P, Suidan MT, Bandy JT, Water Res., 24, 827 (1990)
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Saucedo RA, Ramirez NR, Manzanares L, Bautista RG, Nevarez GV, Environ. Technol., 24, 457 (2003)
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Hamed MM, Khalafallah MG, Hassanien EA, Environ.Modell. Softw., 19, 919 (2004)
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Maier HR, Dandy GC, Environ. Modell. Softw., 13, 179 (1998)
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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)
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[1] Buisman CJN, Peter I, Anne H, Janssen AJH, Robert TH, Lettinga G, Biotechnol. Bioeng., 38, 813 (1991)

[2] Kuenen JG, Plant Soil., 43, 49 (1975)

[3] TICHY R, JANSSEN A, GROTENHUIS JTC, LETTINGA G, RULKENS WH, Bioresour. Technol., 48(3), 221 (1994)

[4] Fox P, Suidan MT, Bandy JT, Water Res., 24, 827 (1990)

[5] Tavares CRG, Sant’Anna GL, Capdeville B, Water Res., 29, 2293 (1995)

[6] Schreyer HB, Coughlin RW, Biotechnol. Bioeng., 63(2), 129 (1999)

[7] Lertpocasombut K, Capdeville B, Roques H, Application of aerobic biofilm growth in a three-phase fluidized-bed reactor for biological wastewater treatment, 2nd IAWPRC Asian Conf. Water Pollut. Control, Bangkok (1988)

[8] Fan LS, Tang WT, AIChE J., 35, 355 (1989)

[9] Saucedo RA, Ramirez NR, Manzanares L, Bautista RG, Nevarez GV, Environ. Technol., 24, 457 (2003)

[10] Mowla D, Ahmadi M, Biochem. Eng. J., 36, 147 (2007)

[11] Rajasimman M, Karthikeyan C, J. Hazard. Mater., 143(1-2), 82 (2007)

[12] Rajasimman M, Karthikeyan C, J. Appl. Sci. Environ. Manage., 11(3), 97 (2007)

[13] Hamed MM, Khalafallah MG, Hassanien EA, Environ.Modell. Softw., 19, 919 (2004)

[14] Strik DPBTB, Domnanovich AM, Zani L, Braun R, Holubar P, Environ. Modell. Softw., 20, 803 (2005)

[15] Sahinkaya E, Ozkaya B, Kaksonen AH, Puhakka JA, Biotechnol. Bioeng., 97(4), 780 (2007)

[16] Mahmood Q, Zheng P, Wu DL, Wang XS, Yousaf H, Ul-Islam E, Hassan MJ, Jilani G, Azim MR, Biomed. Environ.Sci., 20, 398 (2007)

[17] Rangasamy P, Iyer PVR, Ganesan S, J. Environ. Sci., 19, 1416 (2007)

[18] Shi X, Qiao J, Neural network predictive optimal control for wastewater treatment, International Conference on Intelligent Control and Information Processing, August 13-15, Dalian, China (2010)

[19] Vinod AV, Kumar KA, Reddy GV, Biochem. Eng. J., 46, 12 (2009)

[20] Wang AJ, Liu CS, Han HJ, Ren NQ, Lee DJ, J. Hazard. Mater., 168(2-3), 1274 (2009)

[21] Delnavaz M, Ayati B, Ganjidoust H, J. Hazard. Mater., 179(1-3), 769 (2010)

[22] Berastegi GI, Elias A, Arias R, Barona A, IEEE, 584 (2007)

[23] Sreekrishnan TR, Ramachandran KB, Ghosh P, Biotechnol.Bioeng., 37, 557 (1991)

[24] Midha V, Jha MK, Dey A, J. Eng. Sci. Technol., Inpress.

[25] Maier HR, Dandy GC, Environ. Modell. Softw., 13, 179 (1998)

[26] 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)

[27] Abdi H, Valentin D, Edelman B, O’Toole AJ, J. Math., 40, 175 (1996)

[28] El-Din AG, Smith DW, Water Res., 36, 1115 (2002)

[29] Toth E, Brath A, Montanari A, J. Hydrol., 239, 132 (2000)

[30] Guha A, Application of artificial neural network for predicting yarn properties and process parameters, Ph.D Thesis, Indian Institute of Technology, Delhi, India (2002)

[31] Midha V, Jha MK, Dey A, J. Environ. Sci., 24, 513 (2012)