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Journal of Environmental Science and Health, Part A
Toxic/Hazardous Substances and Environmental Engineering
Volume 52, 2017 - Issue 1
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ARTICLES

Application of fuzzy neural networks for modeling of biodegradation and biogas production in a full-scale internal circulation anaerobic reactor

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Pages 7-14 | Received 04 Jun 2016, Accepted 14 Jul 2016, Published online: 09 Sep 2016

References

  • Erdirencelebi, D.; Yalpir, S. Adaptive network fuzzy inference system modeling for the input selection and prediction of anaerobic digestion effluent quality. Appl. Math. Model. 2011, 35, 3821–3832.
  • Huang, M.Z.; Ma, Y.W.; Wang, Y.; Wan, J.Q.; Zhang, H.P. The fate of di-n-butyl phthalate in a laboratory-scale anaerobic/anoxic/oxic wastewater treatment process. Bioresour. Technol. 2010, 101, 7767–7772.
  • Liu, X.P.; Ma, L.; Li, X.; Ai, B.; Li, S.Y.; He, Z.J. Simulating urban growth by integrating landscape expansion index (LEI) and cellular automata. Int. J. Geogr. Inf. Sci. 2014, 28, 148–163.
  • Cakmakci, M. Adaptive neuro-fuzzy modelling of anaerobic digestion of primary sedimentation sludge. Bioprocess. Biosyst. Eng. 2007, 30, 349–357.
  • Chan, C.W.; Huang, G.. H. Artificial intelligence for management and control of pollution minimization and mitigation processes. Eng. Appl. Artific. Intel. 2003, 6, 75–90.
  • Turkdogan-Aydınol, F. I.; Yetilmezsoy, K. A fuzzy-logic-based model to predict biogas and methane production rates in a pilot-scale mesophilic UASB reactor treating molasses wastewater. J. Hazard. Mater. 2010, 182, 460–471.
  • Huang, M.Z.; Han, W.; Wan, J.Q.; Ma, Y.W.; Chen, X.H. Multi-objective optimization for design and operation of anaerobic digestion using GA-ANN and NSGA-II. J. Chem. Technol. Biotechnol. 2016, 91, 226–233.
  • Bolle, W.L.; van Breugel, J.; van Eybergen, G.C.; Kossen, N.W. F.; van Gils, W. An integral dynamic model for the UASB reactor. Biotechnol. Bioeng. 1986, 2, 542–548.
  • Narnoli, S.K.; Mehrotra, I. Sludge blanket of UASB reactor: mathematical simulation. Water Res. 1997, 31, 715–726.
  • Costello, D.J.; Greenfield, P.F.; Lee, P.L. Dynamic modeling of a single stage high rate anaerobic reactor. I. Model derivation. Water Res. 1991, 25, 847–858.
  • Kalyuzhnyi, S.V.; Davlyatshina, M.A. Batch anaerobic digestion of glucose and its mathematical modeling. I. Kinetic investigations. Bioresour. Technol. 1997, 59, 73–80.
  • Huang, K.; Liu, X.P.; Li, X.; Liang, J.Y. An improved artificial immune system for seeking the Pareto front of land use allocation problem in large areas. Int. J. Geogr. Inf. Sci. 2013, 27, 922–946.
  • Shi, Y.; Zhao, X.T.; Zhang, Y.M.; Ren, N.Q. Back propagation neural network (BPNN) prediction model and control strategies of methanogen phase reactor treating traditional Chinese medicine wastewater(TCMW). J. Biotechnol. 2009, 144, 70–74.
  • Sridevi, K.; Sivaraman, E.; Mullai, P. Back propagation neural network modelling of biodegradation and fermentative biohydrogen production using distillery wastewater in a hybrid upflow anaerobic sludge blanket reactor. Bioresour. Technol. 2014, 165, 233–240.
  • Sedighi, M.; Keyvanloo, K.; Towfighi, J. Modeling of Thermal cracking of heavy liquid hydrocarbon: application of kinetic modeling, artificial neural network, and neuro-fuzzy models. Ind. Eng. Chem. Res. 2011, 50, 1536–1547.
  • Huang, M.Z.; Wan, J.Q.; Ma, Y.W.; Wang, Y.; Li, W.J.; Sun, X.F. Control rules of aeration in a submerged biofilm wastewater treatment process using fuzzy neural networks. Expert Sys. App. 2009, 36, 10428–10437.
  • Wan, J.Q.; Huang, M.Z.; Ma, Y.W.; Guo, W. J.; Wang, Y.; Zhang, H.P.; Li, W.J.; Sun, X.F. Prediction of effluent quality of a paper mill wastewater treatment using an adaptive network-based fuzzy inference system. Appl. Soft. Comput. 2011, 11, 3238–3246.
  • Huang, M.Z.; Ma, Y.W.; Wan, J.Q. H.P Zhang, Wang, Y.; Chen, Y.M.; Yoo, C.K.; Guo, W.J. A hybrid genetic—Neural algorithm for modeling the biodegradation process of DnBP in AAO system. Bioresour. Technol. 2011, 102, 8907–8913.
  • Cai, J.; Zheng, P.; Qaisar, M.; Luo, T. Prediction and quantifying parameter importance in simultaneous anaerobic sulfide and nitrate removal process using artificial neural network. Environ. Sci. Pollut. Res. 2015, 22, 8272–8279.
  • Perendeci, A.; Arslan, S.; Tanyolaç, A.; Çelebi, S.S. Effects of phase vector and history extension on prediction power of adaptive-network based fuzzy inference system (ANFIS) model for a real scale anaerobic wastewater treatment plant operating under unsteady state. Bioresour. Technol. 2009, 100, 4579–4587.
  • Perendeci, A.; Arslan, S.; Çelebi, S.S.; Tanyolaç, A. Prediction of effluent quality of an anaerobic treatment plant under unsteady state through ANFIS modeling with on-line input variables. Chem. Eng. J. 2008, 145, 78–85.
  • Mullai, P.; Arulselvi, S.; Ngo, H.H.; Sabarathinam, P.L. Experiments and ANFIS modelling for the biodegradation of penicillin-G wastewater using anaerobic hybrid reactor. Bioresour. Technol. 2011, 102, 5492–5497.
  • China's State Environmental Protection Administration. Standard Methods for the Examination of Water Wastewater. China Environmental Science Press, Beijing, 2002.
  • Jang, S.R. ANFIS: Adaptive-network-based fuzzy inference systems. IEEE T. Syst. Man. C. 1993, 23, 665–685.
  • Yetilmezsoy, K.; Sapci-Zengin, Z. Stochastic modeling applications for the prediction of COD removal efficiency of UASB reactors treating diluted real cotton textile wastewater. Stochas. Environ. Res. Risk Assess. 2009, 23, 13–26.
  • Yetilmezsoy, K.; Sakar, S. Development of empirical models for performance evaluation of UASB reactors treating poultry manure wastewater under different operational conditions. J. Hazard. Mater. 2008, 153, 532–543.
  • Biswas, J.; Chowdhury, R.; Bhattacharya, P. Mathematical modeling for the prediction of biogas generation characteristics of an anaerobic digester based on food/vegetable residues. Biomass Bioenerg. 2007, 31, 80–86.

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