Artificial neural network modelling for organic and total nitrogen removal of aerobic granulation under steady-state condition

References

• Yilmaz G, Lemaire R, Keller J, et al. Simultaneous nitrification, denitrification, and phosphorus removal from nutrient-rich industrial wastewater using granular sludge. Biotechnol Bioeng. 2008;100:529–541. doi: 10.1002/bit.21774
   PubMed Web of Science ®Google Scholar
• Val del Río A, Figueroa M, Arrojo B, et al. Aerobic granular SBR systems applied to the treatment of industrial effluents. J Environ Manage. 2012;95(Suppl):S88–S92. doi: 10.1016/j.jenvman.2011.03.019
   PubMedGoogle Scholar
• Nancharaiah YV, Schwarzenbeck N, Mohan TVK, et al. Biodegradation of nitrilotriacetic acid (NTA) and ferric-NTA complex by aerobic microbial granules. Water Res. 2006;40:1539–1546. doi: 10.1016/j.watres.2006.02.006
   PubMed Web of Science ®Google Scholar
• Muda K, Aris A, Salim MR, et al. The effect of hydraulic retention time on granular sludge biomass in treating textile wastewater. Water Res. 2011;45:4711–4721. doi: 10.1016/j.watres.2011.05.012
   PubMed Web of Science ®Google Scholar
• Liu L, Wang Z, Lin K, et al. Microbial degradation of polyacrylamide by aerobic granules. Environ Technol. 2012;33:1049–1054. doi: 10.1080/09593330.2011.606846
   PubMed Web of Science ®Google Scholar
• Lee DJ, Ho KL, Chen YY. Degradation of cresols by phenol-acclimated aerobic granules. Appl Microbiol Biotechnol. 2011;89:209–215. doi: 10.1007/s00253-010-2878-7
   PubMed Web of Science ®Google Scholar
• Ni BJ, Yu HQ. Mathematical modeling of aerobic granular sludge: a review. Biotechnol Adv. 2010;28:895–909. doi: 10.1016/j.biotechadv.2010.08.004
   PubMed Web of Science ®Google Scholar
• Filali A, Mañas A, Mercade M, et al. Stability and performance of two GSBR operated in alternating anoxic/aerobic or anaerobic/aerobic conditions for nutrient removal. Biochem Eng J. 2012;67:10–19. doi: 10.1016/j.bej.2012.05.001
   Web of Science ®Google Scholar
• de Bruin LMM, de Kreuk MK, van der Roest HFR, et al. Aerobic granular sludge technology: an alternative to activated sludge? Water Sci Technol. 2004;49:1–7. doi: 10.2166/wst.2004.0790
   PubMed Web of Science ®Google Scholar
• de Kreuk MK, Heijnen JJ, van Loosdrecht MCM. Simultaneous COD, nitrogen, and phosphate removal by aerobic granular sludge. Biotechnol Bioeng. 2005;90:761–769. doi: 10.1002/bit.20470
   PubMed Web of Science ®Google Scholar
• Li Y, Liu Y, Shen L, et al. DO diffusion profile in aerobic granule and its microbiological implications. Enzyme Microb Technol. 2008;43:349–354. doi: 10.1016/j.enzmictec.2008.04.005
   Web of Science ®Google Scholar
• Ho KL, Chen YY, Lin B, et al. Degrading high-strength phenol using aerobic granular sludge. Appl Microbiol Biotechnol. 2010;85:2009–2015. doi: 10.1007/s00253-009-2321-0
   PubMed Web of Science ®Google Scholar
• Tay J, Pan S, He Y, et al. Effect of organic loading rate on aerobic granulation. I: reactor performance. J Environ Eng. 2004;130:1094–1101. doi: 10.1061/(ASCE)0733-9372(2004)130:10(1094)
   Web of Science ®Google Scholar
• Tabatabaei M, Sulaiman A, Nikbakht AM, et al. Influential parameters on biomethane generation in anaerobic wastewater treatment plants. In: Manzanera M, editor. Alternative fuel. Rijeka: InTech; 2011. p. 227–262.
   Google Scholar
• Chen Y, Jiang W, Liang DT, et al. Aerobic granulation under the combined hydraulic and loading selection pressures. Bioresour Technol. 2008;99:7444–7449. doi: 10.1016/j.biortech.2008.02.028
   PubMed Web of Science ®Google Scholar
• Wei D, Qiao Z, Zhang Y, et al. Effect of COD/N ratio on cultivation of aerobic granular sludge in a pilot-scale sequencing batch reactor. Appl Microbiol Biotechnol. 2013;97:1745–1753. doi: 10.1007/s00253-012-3991-6
   PubMed Web of Science ®Google Scholar
• Wu L, Peng C, Peng Y, et al. Effect of wastewater COD/N ratio on aerobic nitrifying sludge granulation and microbial population shift. J Environ Sci. 2012;24:234–241. doi: 10.1016/S1001-0742(11)60719-5
   Web of Science ®Google Scholar
• Liu Y, Wang ZW, Liu YQ, et al. A generalized model for settling velocity of aerobic granular sludge. Biotechnol Prog. 2005;21:621–626. doi: 10.1021/bp049674u
   PubMed Web of Science ®Google Scholar
• Liu XW, Sheng GP, Yu HQ. Physicochemical characteristics of microbial granules. Biotechnol Adv. 2009;27(6):1061–1070. doi: 10.1016/j.biotechadv.2009.05.020
   PubMed Web of Science ®Google Scholar
• Li AJ, Li XY. Selective sludge discharge as the determining factor in SBR aerobic granulation: numerical modelling and experimental verification. Water Res. 2009;43:3387–3396. doi: 10.1016/j.watres.2009.05.004
   PubMed Web of Science ®Google Scholar
• Tay JH, Yang SF, Liu Y. Hydraulic selection pressure-induced nitrifying granulation in sequencing batch reactors. Appl Microbiol Biotechnol. 2002;59:332–337. doi: 10.1007/s00253-002-0996-6
   PubMed Web of Science ®Google Scholar
• Tay JH, Liu QS, Liu Y. The effects of shear force on the formation, structure and metabolism of aerobic granules. Appl Microbiol Biotechnol. 2001;57:227–233. doi: 10.1007/s002530100766
   PubMed Web of Science ®Google Scholar
• Liu YQ, Wu WW, Tay JH, et al. Starvation is not a prerequisite for the formation of aerobic granules. Appl Microbiol Biotechnol. 2007;76:211–216. doi: 10.1007/s00253-007-0979-8
   PubMed Web of Science ®Google Scholar
• Liu YQ, Tay JH. Characteristics and stability of aerobic granules cultivated with different starvation time. Appl Microbiol Biotechnol. 2007;75:205–210. doi: 10.1007/s00253-006-0797-4
   PubMed Web of Science ®Google Scholar
• Adav SS, Lee DJ, Lai JY. Potential cause of aerobic granular sludge breakdown at high organic loading rates. Appl Microbiol Biotechnol 2010;85:1601–1610. doi: 10.1007/s00253-009-2317-9
   PubMed Web of Science ®Google Scholar
• Juang YC, Adav SS, Lee DJ, et al. Stable aerobic granules for continuous-flow reactors: precipitating calcium and iron salts in granular interiors. Bioresour Technol. 2010;101:8051–8057. doi: 10.1016/j.biortech.2010.05.078
   PubMed Web of Science ®Google Scholar
• Elnekave M, Celik SO, Tatlier M, et al. Artificial neural network predictions of up-flow anaerobic sludge blanket (UASB) reactor performance in the treatment of citrus juice wastewater. Polish J Environ Stud. 2012;21:49–56.
   Web of Science ®Google Scholar
• Guwy AJ, Hawkes FR, Wilcox SJ, et al. Neural network and on-off control of bicarbonate alkalinity in a fluidised-bed anaerobic digester. Water Res. 1997;31:2019–2025. doi: 10.1016/S0043-1354(97)00016-X
   Web of Science ®Google Scholar
• Kanat G, Saral A. Estimation of biogas production rate in a thermophilic UASB reactor using artificial neural networks. Environ Model Assess. 2009;14:607–614. doi: 10.1007/s10666-008-9150-x
   Web of Science ®Google Scholar
• Mahmod N, Wahab NA. Dynamic modelling of aerobic granular sludge artificial neural networks. Int J Elec Comput Eng. 2017;7:1568–1573.
   Google Scholar
• Tay JH, Zhang X. Neural fuzzy modeling of anaerobic biological wastewater treatment systems. J Environ Eng. 1999;125:1149–1159. doi: 10.1061/(ASCE)0733-9372(1999)125:12(1149)
   Web of Science ®Google Scholar
• Hamed MM, Khalafallah MG, Hassanien EA. Prediction of wastewater treatment plant performance using artificial neural networks. Environ Model Softw. 2004;19:919–928. doi: 10.1016/j.envsoft.2003.10.005
   Web of Science ®Google Scholar
• Sammut C, Webb GI, eds. Encyclopedia of machine learning. Boston (MA): Springer; 2010.
   Google Scholar
• Miikkulainen R. Topology of a neural network. In: Sammut C, Webb GI, editors. Encyclopedia of machine learning. Boston (MA): Springer; 2010. p. 988–989.
   Google Scholar
• Demuth H, Beale M, Hagan M. Neural network Toolbox™ 6 user’s guide. Natick (MA): The MathWorks, Inc; 2009.
   Google Scholar
• Maier H, Dandy G. Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environ Model Softw. 2000;15:101–124. doi: 10.1016/S1364-8152(99)00007-9
   Web of Science ®Google Scholar
• Roadknight CM, Balls GR, Mills GE, et al. Modeling complex environmental data. IEEE Trans. Neural Netw. 1997;8:852–862. doi: 10.1109/72.595883
   PubMedGoogle Scholar
• Maier HR, Dandy GC, Burch MD. Use of artificial neural networks for modelling cyanobacteria anabaena spp. in the River Murray, South Australia. Ecol Modell. 1998;105:257–272. doi: 10.1016/S0304-3800(97)00161-0
   Web of Science ®Google Scholar
• Masters T. Practical neural network recipes in C++. San Diego (CA): Academic Press; 1993.
   Google Scholar
• Lachtermacher G, Fuller JD. Backpropagation in hydrological time series forecasting. In: Hipel KW, McLeod AI, Panu US, et al., editors. Stochastic and statistical methods in hydrology and environmental engineering. Dordrecht: Kluwer Academic; 1994. p. 229–242.
   Google Scholar
• Maier HR, Dandy GC. Determining inputs for neural network models of multivariate time series. Comput Civ Infrastruct Eng. 1997;12:353–368. doi: 10.1111/0885-9507.00069
   Google Scholar
• Lipton ZC, Berkowitz J, Elkan C. A critical review of recurrent neural networks for sequence learning; 2015.
   Google Scholar
• Sarle WS. Neural networks and statistical models. Proceedings of the Nineteenth Annual SAS Users Group International Conference; 1994. p. 1538–1550.
   Google Scholar
• Solla SA, Levin E, Fleisher M. Accelerated learning in layered neural networks. Complex Syst. 1988;2:625–639.
   Google Scholar
• Heskes TM, Kappen B. On-line learning processes in artificial neural networks. In: Taylor JG, editor. Mathematical approaches to neural networks. Amsterdam: Elsevier; 1993. p. 199–233.
   Google Scholar
• Kirkpatrick S, Gilatt CD, Vecchi MP. Optimization by simulated annealing. Science. 1983;220:671–680. doi: 10.1126/science.220.4598.671
   PubMed Web of Science ®Google Scholar
• Goldberg D. Genetic algorithms. Reading (MA): Addison-Wesley; 1989.
   Google Scholar
• Parisi R, Di Claudio ED, Orlandi G, et al. A generalized learning paradigm exploiting the structure of feedforward neural networks. IEEE Trans Neural Netw. 1996;7:1450–1460. doi: 10.1109/72.548172
   PubMed Web of Science ®Google Scholar
• Golden RM. Mathematical methods for neural network analysis and design. Cambridge: MIT Press; 1996.
   Google Scholar
• Yu H, Wilamowski BM. Neural network training with second order algorithms. Berlin: Springer; 2012. p. 463–476.
   Google Scholar
• Shanno DF. Conjugate gradient methods with inexact searches. Math Oper Res. 1978;3:244–256. doi: 10.1287/moor.3.3.244
   Google Scholar
• Hoaglin DC, Iglewicz B. Fine-tuning some resistant rules for outlier labeling. J Am Stat Assoc. 1987;82:1147–1149. doi: 10.1080/01621459.1987.10478551
   Web of Science ®Google Scholar
• Seo S. A review and comparison of methods for detecting outliers in univariate data sets; 2006.
   Google Scholar
• Huang W, Lai KK, Nakamori Y, et al. Forecasting foreign exchange rates with artificial neural networks: a review. Int J Inf Technol Decis Mak. 2004;3:145–165. doi: 10.1142/S0219622004000969
   Web of Science ®Google Scholar
• Hannan EJ, Quinn BG. The determination of the order of an autoregression. J R Stat Soc Series B Methodol. 1979;41:190–195.
   Google Scholar
• Hurvich CM, Tsai CL. Regression and time series model selection in small samples. Biometrika. 1989;76:297–307. doi: 10.1093/biomet/76.2.297
   Web of Science ®Google Scholar
• Cybenkot G. Approximation by superpositions of a sigmoidal function. Math Control Signal Syst. 1989;2:303–314. doi: 10.1007/BF02551274
   Google Scholar
• Bathe S, de Kreuk M, McSwain B, et al. Aerobic granular sludge. London: IWA Publishing; 2005.
   Google Scholar
• Wu W, May RJ, Maier HR, et al. A benchmarking approach for comparing data splitting methods for modeling water resources parameters using artificial neural networks. Water Resour Res. 2013;49:7598–7614. doi: 10.1002/2012WR012713
   Web of Science ®Google Scholar
• Puzyn T, Mostrag-Szlichtyng A, Gajewicz A, et al. Investigating the influence of data splitting on the predictive ability of QSAR/QSPR models. Struct Chem. 2011;22:795–804. doi: 10.1007/s11224-011-9757-4
   Web of Science ®Google Scholar
• Sprevak D, Azuaje F, Wang H. A non-random data sampling method for classification model assessment. Vol. 3, Proceedings of the 17th International Conference on Pattern Recognition (ICPR); 2004. p. 406–409.
   Google Scholar
• Snee RD. Validation of regression models: methods and examples. Technometrics. 1977;19:415–428. doi: 10.1080/00401706.1977.10489581
   Web of Science ®Google Scholar
• May RJ, Maier HR, Dandy GC. Data splitting for artificial neural networks using SOM-based stratified sampling. Neural Netw. 2010;23:283–294. doi: 10.1016/j.neunet.2009.11.009
   PubMed Web of Science ®Google Scholar
• Machado VC, Gabriel D, Lafuente J, et al. Cost and effluent quality controllers design based on the relative gain array for a nutrient removal WWTP. Water Res. 2009;43:5129–5141. doi: 10.1016/j.watres.2009.08.011
   PubMed Web of Science ®Google Scholar
• Eckenfelder WW, Argaman Y, Miller E. Process selection criteria for the biological treatment of industrial wastewaters. Environ Prog. 1989;8:40–45. doi: 10.1002/ep.3300080112
   Google Scholar
• Wu W, Maier H, Dandy G, et al. Exploring the impact of data splitting methods on artificial neural network models. 10th International Conference on Hydroinformatics (HIC) 2012, Hamburg, Germany; 2012.
   Google Scholar
• Morales N, Figueroa M, Fra-Vázquez A, et al. Operation of an aerobic granular pilot scale SBR plant to treat swine slurry. Process Biochem. 2013;48:1216–1221. doi: 10.1016/j.procbio.2013.06.004
   Web of Science ®Google Scholar
• Beun JJ, Hendriks A, van Loosdrecht MCM, et al. Aerobic granulation in a sequencing batch reactor. Water Res. 1999;33:2283–2290. doi: 10.1016/S0043-1354(98)00463-1
   Web of Science ®Google Scholar
• Yu X, Wan C, Lei Z, et al. Use of aerobic granules for treating synthetic high-strength ammonium wastewaters. Environ Technol. 2014;35:1785–1790. doi: 10.1080/09593330.2014.882992
   PubMed Web of Science ®Google Scholar
• Su KZ, Yu HQ. Formation and characterization of aerobic granules in a sequencing batch reactor treating soybean-processing wastewater. Environ Sci Technol. 2005;39:2818–2827. doi: 10.1021/es048950y
   PubMed Web of Science ®Google Scholar
• Wang SG, Liu XW, Zhang HY, et al. Aerobic granulation for 2,4-dichlorophenol biodegradation in a sequencing batch reactor. Chemosphere. 2007;69:769–775. doi: 10.1016/j.chemosphere.2007.05.026
   PubMed Web of Science ®Google Scholar
• Liu QS, Liu Y, Tay STL, et al. Startup of pilot-scale aerobic granular sludge reactor by stored granules. Environ Technol. 2005;26:1363–1370. doi: 10.1080/09593332608618616
   PubMed Web of Science ®Google Scholar
• Wang F, Lu S, Wei Y, et al. Characteristics of aerobic granule and nitrogen and phosphorus removal in a SBR. J Hazard Mater. 2009;164:1223–1227. doi: 10.1016/j.jhazmat.2008.09.034
   PubMed Web of Science ®Google Scholar
• Jafari Kang A, Yuan Q. Long-term stability and nutrient removal efficiency of aerobic granules at low organic loads. Bioresour Technol. 2017;234:336–342. doi: 10.1016/j.biortech.2017.03.057
   PubMed Web of Science ®Google Scholar
• Tao J, Qin L, Liu X, et al. Effect of granular activated carbon on the aerobic granulation of sludge and its mechanism. Bioresour Technol. 2017;236:60–67. doi: 10.1016/j.biortech.2017.03.106
   PubMed Web of Science ®Google Scholar
• Wan J, Sperandio M. Possible role of denitrification on aerobic granular sludge formation in sequencing batch reactor. Chemosphere. 2009;75:220–227. doi: 10.1016/j.chemosphere.2008.11.069
   PubMed Web of Science ®Google Scholar
• Li AJ, Yang SF, Li XY, et al. Microbial population dynamics during aerobic sludge granulation at different organic loading rates. Water Res. 2008;42:3552–3560. doi: 10.1016/j.watres.2008.05.005
   PubMed Web of Science ®Google Scholar
• Leong J, Rezania B, Mavinic DS. Aerobic granulation utilizing fermented municipal wastewater under low pH and alkalinity conditions in a sequencing batch reactor. Environ Technol. 2016;37:55–63. doi: 10.1080/09593330.2015.1063704
   PubMed Web of Science ®Google Scholar
• de Kreuk MK, van Loosdrecht MC. Formation of aerobic granules with domestic sewage. J Environ Eng. 2006;132:694–697. doi: 10.1061/(ASCE)0733-9372(2006)132:6(694)
   Web of Science ®Google Scholar
• Arrojo B, Mosquera-Corral A, Garrido JM, et al. Aerobic granulation with industrial wastewater in sequencing batch reactors. Water Res. 2004;38:3389–3399. doi: 10.1016/j.watres.2004.05.002
   PubMed Web of Science ®Google Scholar
• Grieu S, Thiery F, Traoré A, et al. KSOM and MLP neural networks for on-line estimating the efficiency of an activated sludge process. Chem Eng J. 2006;116:1–11. doi: 10.1016/j.cej.2005.10.004
   Web of Science ®Google Scholar
• Güçlü D, Dursun S. Amelioration of carbon removal prediction for an activated sludge process using an artificial neural network (ANN). Clean – Soil, Air, Water. 2008;36:781–787. doi: 10.1002/clen.200700155
   Web of Science ®Google Scholar
• Libotean D, Giralt J, Giralt F, et al. Neural network approach for modeling the performance of reverse osmosis membrane desalting. J Memb Sci. 2009;326:408–419. doi: 10.1016/j.memsci.2008.10.028
   Web of Science ®Google Scholar
• Yangali-Quintanilla V, Verliefde A, Kim TU, et al. Artificial neural network models based on QSAR for predicting rejection of neutral organic compounds by polyamide nanofiltration and reverse osmosis membranes. J Memb Sci. 2009;342:251–262. doi: 10.1016/j.memsci.2009.06.048
   Web of Science ®Google Scholar