Prediction and quantifying parameter importance in simultaneous anaerobic sulfide and nitrate removal process using artificial neural network

Environmental Science and Pollution Research

Volumn 22, Issue 11, 2015, Pages 8272-8279

  Cai, Jing ^a   Zheng, Ping ^b   Qaisar, Mahmood ^c   Luo, Tao ^a  

^a ZHEJIANG GONGSHANG UNIVERSITY   (China)

^b ZHEJIANG UNIVERSITY   (China)

^c Tobe Camp University Road Abbottabad   (Pakistan)

Author keywords

Artificial neural network; Modeling; Parameter importance; Simultaneous anaerobic sulfide and nitrate removal; UASB reactor

Indexed keywords

ALGORITHM; ANOXIC CONDITIONS; ARTIFICIAL NEURAL NETWORK; BIOREACTOR; NITRATE; POLLUTANT REMOVAL; PREDICTION; QUANTITATIVE ANALYSIS; SULFIDE;

NITRIC ACID DERIVATIVE; NITROGEN; NITROGEN OXIDE; SEWAGE; SULFATE; SULFIDE;

ALGORITHM; ANAEROBIC GROWTH; ARTIFICIAL NEURAL NETWORK; BIOREACTOR; CHEMISTRY; EVALUATION STUDY; METABOLISM; PROCEDURES; SEWAGE;

ALGORITHMS; ANAEROBIOSIS; BIOREACTORS; EVALUATION STUDIES AS TOPIC; NEURAL NETWORKS (COMPUTER); NITRATES; NITROGEN; NITROGEN OXIDES; SEWAGE; SULFATES; SULFIDES; WASTE DISPOSAL, FLUID;

EID: 84929839399     PISSN: 09441344     EISSN: 16147499     Source Type: Journal    
DOI: 10.1007/s11356-014-3976-3     Document Type: Article

References (31)

1. An S, Tang K, Nemati M (2010) Simultaneous biodesulphurization and denitrification using an oil reservoir microbial culture: effects of sulphide loading rate and sulphide to nitrate loading ratio. Water Res 44:1531–1541. doi:10.1016/j.watres.2009.10.037
2. APHA, AWWA, WPCF (1998) Standard methods for the examination of water and wastewater, 20th edn. American Public Health Association, Washington, DC
3. Baspinar AB, Turker M, Hocalar A, Ozturk I (2011) Biogas desulphurization at technical scale by lithotrophic denitrification: integration of sulphide and nitrogen removal. Process Biochem 46:916–922. doi:10.1016/j.procbio.2011.01.001
4. Cai J, Zheng P, Mahmood Q, Islam E, Hu BL, Wu DL (2007) Effects of loading rate and hydraulic residence time on anoxic sulfide biooxidation. J Zhejiang Univ Sci A 8:1149–1156. doi:10.1631/jzus.2007.A1149
5. Cai J, Zheng P, Mahmood Q (2008) Effect of sulfide to nitrate ratios on the simultaneous anaerobic sulfide and nitrate removal. Bioresource Technol 99:5520–5527. doi:10.1016/j.biortech.2007.10.053
6. Cai J, Zheng P, Mahmood Q (2010) Influence of transient pH and substrate shocks on simultaneous anaerobic sulfide and nitrate removal. J Hazard Mater 174:162–166. doi:10.1016/j.jhazmat.2009.09.031
7. Campos JL, Carvalho S, Portela R, Mosquera-Corral A, Méndez R (2008) Kinetics of denitrification using sulphur compounds: effects of S/N ratio, endogenous and exogenous compounds. Bioresource Technol 99:1293–1299. doi:10.1016/j.biortech.2007.02.007
8. Cirne DG, van der Zee FP, Fernandez-Polanco M, Fernandez-Polanco F (2008) Control of sulphide during anaerobic treatment of S-containing wastewaters by adding limited amounts of oxygen or nitrate. Rev Environ Sci Bio 7:93–105. doi:10.1007/s11157-008-9128-9
9. Garcia de Lomas J, Corzo A, Gonzalez JM, Andrades JA, Iglesias E, Montero MJ (2006) Nitrate promotes biological oxidation of sulfide in wastewaters: experiment at plant-scale. Biotechnol Bioeng 93:801–811
10. Garcia-de-Lomas J, Corzo A, Portillo MC, Gonzalez JM, Andrades JA, Saiz-Jimenez C, Garcia-Robledo E (2007) Nitrate stimulation of indigenous nitrate-reducing, sulfide-oxidising bacterial community in wastewater anaerobic biofilms. Water Res 41:3121–3131. doi:10.1016/j.watres.2007.04.004
11. Gevrey M, Dimopoulos I, Lek S (2003) Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecol Model 160:249–264. doi:10.1016/S0304-3800(02)00257-0
12. Jin RC, Zheng P (2009) Kinetics of nitrogen removal in high rate anammox upflow filter. J Hazard Mater 170:652–656. doi:10.1016/j.jhazmat.2009.05.016
13. Jin RC, Yang GF, Yu JJ, Zheng P (2012) The inhibition of the anammox process: a review. Chem Eng J 197:67–79. doi:10.1016/j.cej.2012.05.014
14. Kundu P, Debsarkar A, Mukherjee S, Kumar S (2014) Artificial neural network modelling in biological removal of organic carbon and nitrogen for the treatment of slaughterhouse wastewater in a batch reactor. Environ Technol 35:1296–1306. doi:10.1080/09593330.2013.866698
15. Mahmood Q, Zheng P, Cai J, Hayat Y, Hassan MF, Wu DL, Hu BL (2007a) Sources of sulfide in waste streams and current biotechnologies for its removal. J Zhejiang Univ Sci A 8:1126–1140. doi:10.1631/jzus.2007.A1126
16. Mahmood Q, Zheng P, Cai J, Wu D, Hu B, Li J (2007b) Anoxic sulfide biooxidation using nitrite as electron acceptor. J Hazard Mater 147:249–256. doi:10.1016/j.jhazmat.2007.01.002
17. Meighani HM, Dehghani A, Rekabdar F, Hemmati M, Goodarznia I (2013) Artificial intelligence vs. classical approaches: a new look at the prediction of flux decline in wastewater treatment. Desalin Water Treat 51:7476–7489. doi:10.1080/19443994.2013.773861
18. Midha V, Jha MK, Dey A (2013) Neural network prediction of fluidized bed bioreactor performance for sulfide oxidation. Korean J Chem Eng 30:385–391. doi:10.1007/s11814-012-0128-7
19. Moraes BS, Souza TSO, Foresti E (2012) Effect of sulfide concentration on autotrophic denitrification from nitrate and nitrite in vertical fixed-bed reactors. Process Biochem 47:1395–1401. doi:10.1016/j.procbio.2012.05.008
20. Mu Y, Yu HQ (2007) Simulation of biological hydrogen production in a UASB reactor using neural network and genetic algorithm. Int J Hydrogen Energ 32:3308–3314. doi:10.1016/j.ijhydene.2007.05.021
21. Mullai P, Arulselvi S, Ngo HH, Sabarathinam PL (2011) Experiments and ANFIS modelling for the biodegradation of penicillin-G wastewater using anaerobic hybrid reactor. Bioresource Technol 102:5492–5497. doi:10.1016/j.biortech.2011.01.085
22. Mustafa YA, Jaid GM, Alwared AI, Ebrahim M (2014) The use of artificial neural network (ANN) for the prediction and simulation of oil degradation in wastewater by AOP. Environ Sci Pollut R 21:7530–7537. doi:10.1007/s11356-014-2635-z
23. Nemati M, Jenneman GE, Voordouw G (2001) Mechanistic study of microbial control of hydrogen sulfide production in oil reservoirs. Biotechnol Bioeng 74:424–434. doi:10.1002/bit.1133
24. Olden JD, Joy MK, Death RG (2004) An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data. Ecol Model 178:389–397. doi:10.1016/j.ecolmodel.2004.03.013
25. Pikaar I, Rozendal RA, Yuan Z, Keller J, Rabaey K (2011) Electrochemical sulfide removal from synthetic and real domestic wastewater at high current densities. Water Res 45:2281–2289. doi:10.1016/j.watres.2010.12.025
26. Shi Y, Zhao XT, Zhang YM, Ren NQ (2009) Back propagation neural network (BPNN) prediction model and control strategies of methanogen phase reactor treating traditional Chinese medicine wastewater (TCMW). J Biotechnol 144:70–74. doi:10.1016/j.jbiotec.2009.08.014
27. Tang K, Baskaran V, Nemati M (2009) Bacteria of the sulphur cycle: an overview of microbiology, biokinetics and their role in petroleum and mining industries. Biochem Eng J 44:73–94. doi:10.1016/j.bej.2008.12.011
28. Wang AJ, Du DZ, Ren NQ, van Groenestijn JW (2005) An innovative process of simultaneous desulfurization and denitrification by Thiobacillus denitrificans. J Environ Sci Health A Tox Hazard Subst Environ Eng 40:1939–1949. doi:10.1080/10934520500184590
29. Wang A, Liu C, Han H, Ren N, Lee DJ (2009) Modeling denitrifying sulfide removal process using artificial neural networks. J Hazard Mater 168:1274–1279. doi:10.1016/j.jhazmat.2009.03.006
30. Yue ZB, Yu HQ, Harada H, Li YY (2007) Optimization of anaerobic acidogenesis of an aquatic plant, Canna indica L., by rumen cultures. Water Res 41:2361–2370. doi:10.1016/j.watres.2007.02.031
31. Zhang B, Zhang J, Yang Q, Feng C, Zhu Y, Ye Z, Ni J (2012) Investigation and optimization of the novel UASB–MFC integrated system for sulfate removal and bioelectricity generation using the response surface methodology (RSM). Bioresource Technol 124:1–7. doi:10.1016/j.biortech.2012.08.045