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Environmental Modeling & Assessment

, Volume 20, Issue 6, pp 625–635 | Cite as

Artificial Neural Network Modeling for Predicting Organic Matter in a Full-Scale Up-Flow Anaerobic Sludge Blanket (UASB) Reactor

  • Carlos MendesEmail author
  • Robson da Silva Magalhes
  • Karla Esquerre
  • Luciano Matos Queiroz
Article

Abstract

The aim of this study is to propose a method for constructing Artificial Neural Network (ANN) models and evaluating their performance based on the application of two methods for the selection of the ANN topology: the dynamic division method (cross-validation or dynamics-split) (DDM) and the static-split method (SSM). The two methods are compared and applied to predict the amount of organic matter in an up-flow anaerobic sludge blanket (UASB) reactor operated at full scale. The performance of the ANN models was assessed through the coefficient of multiple determination (R 2), the adjusted coefficient of multiple determination (\(R^{2}_{adj}\)), and the root mean square error (RMSE). The comparison reveals that the DDM accurately selects the best model and reliably assesses its quality.

Keywords

UASB reactor Artificial neural networks Static-split method Dynamic division method 

Notes

Acknowledgments

We would like to thank the Coordination for the Improvement of Higher Education Personnel - CAPES, for its financial support for this research.

References

  1. 1.
    Akratos, C.S., Papaspyros, J.N., & Tsihrintzis, V.A. (2008). An artificial neural network model and design equations for bod and cod removal prediction in horizontal subsurface flow constructed wetlands. Chemical Engineering Journal, 143(1–3), 96–110.CrossRefGoogle Scholar
  2. 2.
    Batstone, D.J., Keller, J., Angelidaki, I., Kalyuzhny, S., Pavlostathis, S., Rozzi, A., Sanders, W., Siegrist, H., & Vavilin, V. (2002). Anaerobic digestion model No. 1 (ADM1). IWA Publishing.Google Scholar
  3. 3.
    Baughman, R., & Liu, Y.A. (1995). Neural networks in bioprocessing and chemical engineering. Book.Google Scholar
  4. 4.
    Bowden, G.J., Maier, H.R., & Dandy, G.C. (2002). Optimal division of data for neural network models in water resources applications. Water Resources Research, 38(2), 1–11.CrossRefGoogle Scholar
  5. 5.
    Bowman, A.W. (1984). An alternative method of cross-validation for the smoothing of density estimates. Biometrika, 71, 353–360.CrossRefGoogle Scholar
  6. 6.
    Busato, R. (2004). Desempenho de um filtro anaeróbio de fluxo ascendente como tratamento de efluente de reator uasb: estudo de caso da ete de imbituva. Master’s thesis, Universidade Federal do Paraná (In Portuguese).Google Scholar
  7. 7.
    Campello, R.P. (2009). Desempenho de reatores anaeróbios de manto de lodo (uasb) operando sob condições de temperaturas t?picas de regiões de clima temperado. Master’s thesis, Universidade Federal do Rio Grande Do Sul. Instituto de Pesquisas Hidráulicas - IPH (In Portuguese).Google Scholar
  8. 8.
    Carrasco, E., Rodríguez, J., Al, A.P., Roca, E., & Lema, J. (2004). Diagnosis of acidification states in an anaerobic wastewater treatment plant using a fuzzy-based expert system. Control Engineering Practice, 12, 59–64.CrossRefGoogle Scholar
  9. 9.
    Celisse, A., & Robin, S. (2008). Nonparametric density estimation by exact leave-p-out cross-validation. Computational Statistics and Data Analysis, 52, 2350–2368.CrossRefGoogle Scholar
  10. 10.
    Chernicharo, C.A.L. (2007). Reatores anaeróbios. Belo Horizonte, MG: Departamento de Engenharia Sanitária e Ambiental - DESA (In Portuguese).Google Scholar
  11. 11.
    Coelho, N., Capela, I., & Droste, R. (2006). Application of adm1 to a uasb treating complex wastewater in different feeding regimes. Water Environment Foundation, 13, 7123–7135.Google Scholar
  12. 12.
    Costa, A., Henriques, A., Alves, T., Maciel Filho, R., & Lima, E. (1999). A hybrid neural model for the optimization of fed-bath fermentations. Brazilian Journal of Chemical Engineering, 16, 53–63.CrossRefGoogle Scholar
  13. 13.
    Côté, M., Grandjean, B.P., Lessard, P., & Thibault, J. (1995). Dynamic modelling of the activated sludge process: Improving prediction using neural networks. Water Research, 29(4), 995–1004.CrossRefGoogle Scholar
  14. 14.
    Donoso-Bravo, A., Mailiera, J., Martinb, C., Rodríguez, J., Aceves-Larae, C.A., & Wouwera, A.V. (2011). Model selection, identification and validation in anaerobic digestion: A review. Water Research, 45, 5347–5364.CrossRefGoogle Scholar
  15. 15.
    Elsayed, K., & Lacor, C. (2011). Modeling, analysis and optimization of aircyclones using artificial neural network, response surface methodology and cfd simulation approaches. Powder Technology, 212(1), 115–133.CrossRefGoogle Scholar
  16. 16.
    Erdirencelebi, D., & Yalpir, S. (2011). Adaptive network fuzzy inference system modeling for the input selection and prediction of anaerobic digestion effluent quality. Applied Mathematical Modelling, 35, 3821–3832.CrossRefGoogle Scholar
  17. 17.
    Esquerre, K.P.O., Seborg, D.E., Bruns, R.E., & Zori, M. (2004). Application of steady-state and dynamic modeling for the prediction of the bod of an aerated lagoon at a pulp and paper mill: Part i. linear approaches. Chemical Engineering Journal, 104(13), 73–81.CrossRefGoogle Scholar
  18. 18.
    Fay, M., & Proschan, M. (2010). Wilcoxon-mann whitney or t-test? On assumptions for hypothesis tests and multiple interpretations of decision rules. Statistics Surveys, 4, 31–39.CrossRefGoogle Scholar
  19. 19.
    Geisser, S. (1975). The predictive sample reuse method with applications. Journal of the American Statistical Association, 70, 320–328.CrossRefGoogle Scholar
  20. 20.
    Gontarski, C., Rodrigues, P., Mori, M., & Prenem, L. (2000). Simulation of an industrial wastewater treatment plant using artificial neural networks. Computers & Chemical Engineering, 24(2–7), 1719–1723.CrossRefGoogle Scholar
  21. 21.
    Goutte, C. (1997). Note on free lunches and cross-validation. Neural Computation, 9(6), 1245–1249.CrossRefGoogle Scholar
  22. 22.
    Güçlü, D., & Dursun, S. (2008). Prediction of wastewater treatment plant performance using artificial neural networks. CLEAN - Soil, Air, Water, 36(9), 781–787.CrossRefGoogle Scholar
  23. 23.
    Häck, M., & Manfred, K. (1996). Estimation of was tewater process parameters using neural networks. Water Science and Technology, 33, 101–115.CrossRefGoogle Scholar
  24. 24.
    Hamed, M.M., Khalafallah, M.G., & Hassanien, E.A. (2004). Prediction of wastewater treatment plant performance using artificial neural networks. Environmental Modelling & Software, 19(10), 919–928.CrossRefGoogle Scholar
  25. 25.
    Hamoda, M.F., Al-Ghusain, I.A., & Hassan, A.H. (1999). Integrated wastewater treatment plant performance evaluation using artificial neural networks. Water Science and Technology, 40, 55–65.CrossRefGoogle Scholar
  26. 26.
    Harada, L.H.P., da Costa, A.C., & Filho, R.M. (2002). Hybrid neural modeling of bioprocesses using functional link networks. Applied Biochemistry and Biotechnology, 98, 1009–1023.CrossRefGoogle Scholar
  27. 27.
    Kalyuzhnyi, S., Fedorovich, V., Lens, P., Hulshoff Pol, L., & Lettinga, G. (1998). Mathematical modelling as a tool to study population dynamics between sulfate reducing and methanogenic bacteria. Biodegradation, 9, 187–199.CrossRefGoogle Scholar
  28. 28.
    Kato, M.T., Field, J.A., Kleerebezem, R., & Lettinga, G. (1994). Treatment of low strength soluble wastewaters in uasb reactors. Journal of Fermentation and Bioengineering, 77(6), 679–686.CrossRefGoogle Scholar
  29. 29.
    Lee, D., & Park, J. (1999). Neural network modelling for on-line estimation of nutrient dynamics in a sequentially operated batch reactor. Biotechnology, 75, 229–239.Google Scholar
  30. 30.
    Lettinga, G. (1996). Sustainable integrated biological wastewater treatment. Water Science and Technology, 33, 85–98.CrossRefGoogle Scholar
  31. 31.
    Lettinga, G., Field, J., van Lier, J., Zeeman, G., & Pol, L.H. (1997). Advanced anaerobic wastewater treatment in the near future. Water Science and Technology, 35, 5–12.CrossRefGoogle Scholar
  32. 32.
    Lettinga, G., & Hulshoff Pol, L. (1986). Advanced reactor design, operation and economy. Water Science and Technology, 18, 99–108.Google Scholar
  33. 33.
    Lettinga, G., & Hulshoff Pol, L. (1991). Uasb process design for various types of wastewater. Water Science and Technology, 21, 87–107.Google Scholar
  34. 34.
    Liao, K.P., & Fildes, R. (2005). The accuracy of a procedural approach to specifying feedforward neural networks for forecasting. Computers & Operations Research, 32(8), 2151–2169.CrossRefGoogle Scholar
  35. 35.
    Liu, Y., Hai-Lou, X., Shu-Fang, Y., & Joo-Hwa, T. (2003). Mechanisms and models for anaerobic granulation in upflow anaerobic sludge blanket reactor. Water Research, 37, 611–673.Google Scholar
  36. 36.
    Lopez, I., & Borzacconi, L. (2009). Modelling a full scale uasb reactor using a cod global balance approach and state observers. Chemical Engineering Journal, 146(1), 1–5.CrossRefGoogle Scholar
  37. 37.
    Lyberatos, G., & Skiadas, I. (1999). Modelling of anaerobic digestion - a review. Global Nest: the International Journal, 1, 63–76.Google Scholar
  38. 38.
    Magalhães, R.S., Fontes, C.H.O., Almeida, L.A.L., Embírucu, M., & Santos, J.M.C. (2010). A model for three-dimensional simulation of acoustic emissions from rotating machine vibration. The Journal of the Acoustical Society of America, 127(6), 3569–3576.CrossRefGoogle Scholar
  39. 39.
    Maier, H.R., & Dandy, G.C. (2000). Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environmental Modelling & Software, 15(1), 101–124.CrossRefGoogle Scholar
  40. 40.
    Mann, H., & Whitney, D. (1947). On a test of whether one of two random variables is stochastically larger than the other. Annals of Mathematical Statistics, 18, 50–60.CrossRefGoogle Scholar
  41. 41.
    Másson, E., & Wang, Y.J. (1990). Introduction to computation and learning in artificial neural networks. European Journal of Operational Research, 47(1), 1–28.CrossRefGoogle Scholar
  42. 42.
    Meade Jr, A.J., & Sonneborn, H.C. (1996). Numerical solution of a calculus of variations problem using the feedforward neural network architecture. Advances in Engineering Software, 27(3), 213–225.CrossRefGoogle Scholar
  43. 43.
    Narendra, K., & Arthasarathyk, P. (1990). Identification and control of dynamical systems using neural networks. IEEE Transactions on Neural Networks, 1, 4–27.CrossRefGoogle Scholar
  44. 44.
    Nikravesh, M., Farell, A., & Stanford, T. (1996). Model identification of nonlinear time variant processes via artificial neural network. Computers & Chemical Engineering, 20(11), 1277–1290.CrossRefGoogle Scholar
  45. 45.
    Perendeci, A., Arslan, S., Tanyolac, A., & Celebi, S. (2009). 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. Bioresource Technology, 100, 4579–4587.CrossRefGoogle Scholar
  46. 46.
    Prechelt, L. (1998). Automatic early stopping using cross validation: quantifying the criteria. Neural Networks, 11(4), 761–767.CrossRefGoogle Scholar
  47. 47.
    Ramos, R.A. (2008). Avaliação da influência da operao de descarte de lodo no desempenho de reatores uasb em estações de tratamento de esgotos no distrito federal. Master’s thesis, Universidade de Brasília (In Portuguese).Google Scholar
  48. 48.
    Reich, Y., & Barai, S. (2000). A methodology for building neural networks models from empirical engineering data. Engineering Applications of Artificial Intelligence, 13(6), 685–694.CrossRefGoogle Scholar
  49. 49.
    Ren, T.T., Mu, Y., Yu, H.Q., Harada, H., & Li, Y.Y. (2008). Dispersion analysis of an acidogenic uasb reactor. Chemical Engineering Journal, 142(2), 182–189.CrossRefGoogle Scholar
  50. 50.
    Rosenblatt, F. (1958). The perceptron—a probabilistic model for information-storage and organization in the brain. Psychological Review, 65, 386–408.CrossRefGoogle Scholar
  51. 51.
    Rudemo, M. (1982). Empirical choice of histograms and kernel density estimators. Journal of Statistics, 9, 65–78.Google Scholar
  52. 52.
    Saravanan, V., & Sreekrishnan, T. (2006). Modelling anaerobic biofilm reactors - a review. Journal of Environmental Management, 81(1), 1–18.CrossRefGoogle Scholar
  53. 53.
    Schenker, B., & Agarwal, M. (1996). Cross-validated structure selection for neural networks. Computers & Chemical Engineering, 20(2), 175–186.CrossRefGoogle Scholar
  54. 54.
    Selmic, R.R., & Lewis, F.L. (2002). Neural network approximation of piecewise continuous functions: Application to friction compensation. IEEE Transactions on Neural Networks, 13, 745–751.CrossRefGoogle Scholar
  55. 55.
    Shahin, M.A., Holger, R.M., & Jaksa, M.B. (2004). Data division for developing neural networks applied to geotechnical engineering. Geotechnical Engineering, 18, 105–114.Google Scholar
  56. 56.
    Shrestha, D.L., Kayastha, N., & Solomatine, D.P. (2009). A novel approach to parameter uncertainty analysis of hydrological models using neural networks. Hydrology and Earth System Sciences, 13, 1235–1248.CrossRefGoogle Scholar
  57. 57.
    Srivastav, R.K., Sudheer, K.P., & Chaubey, I. (2007). A simplified approach to quantifying predictive and parametric uncertainty in artificial neural network hydrologic models. Water Resources Research, 43(10), 1–12.CrossRefGoogle Scholar
  58. 58.
    Stone, C. (1984). An asymptotically optimal window selection rule for kernel density estimates. The Annals of Statistics, 12, 1285–1297.CrossRefGoogle Scholar
  59. 59.
    Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society. Series B (Methodological), 36(2), 111–147.Google Scholar
  60. 60.
    Stone, M. (1977). An asymptotic equivalence of choice of model by cross-validation and akaikes criterion. Journal of the Royal Statistical Society. Series B, 44, 44–47.Google Scholar
  61. 61.
    Turkdogan-Aydinol, F.I., & Yetilmezsoy, K. (2010). A fuzzy-logic-based model to predict biogas and methane production rates in a pilot-scale mesophilic uasb reactor treating molasses wastewater. Journal of Hazardous Materials, 182, 460–471.CrossRefGoogle Scholar
  62. 62.
    Utojo, U., & Bakshi, B. (1995). Neural networks in bioprocessing and chemical engineering, appendix: Connections between neural networks and multivariate statistical methods: An overview. New York: Academic Press.Google Scholar
  63. 63.
    van Haandel A.C., & e Lettinga, G. (1994). Tratamento anaeróbio de esgotos: Um manual para regiões de clima quente. Campina Grande - Paraíba (In Portuguese).Google Scholar
  64. 64.
    Verma, A., Wei, X., & Kusiak, A. (2013). Predicting the total suspended solids in wastewater: a data-mining approach. Engineering Applications of Artificial Intelligence, 26(4), 1366–1372.CrossRefGoogle Scholar
  65. 65.
    Warne, K., Prasad, G., Rezvani, S., & Maguire, L. (2004). Statistical and computational intelligence techniques for inferential model development: a comparative evaluation and a novel proposition for fusion. Engineering Applications of Artificial Intelligence, 17(8), 871–885.CrossRefGoogle Scholar
  66. 66.
    Wilcox, S., Hawkes, D., Hawkes, F., & Guwy, A. (1995). A neural network, based on bicarbonate monitoring, to control anaerobic digestion. Water Research, 29(6), 1465–1470.CrossRefGoogle Scholar
  67. 67.
    Wilcoxon, F. (1949). Some rapid approximate statistical procedures. Stamford, CT: Stamford Research Laboratories.Google Scholar
  68. 68.
    Zeng, G., Qin, X., He, L., Huang, G., Liu, H., & Lin, Y. (2003). A neural network predictive control system for paper mill wastewater treatment. Engineering Applications of Artificial Intelligence, 16, 121–129.CrossRefGoogle Scholar
  69. 69.
    Zhan, J.X., & Ishida, M. (2001). The multi-step predictive control of nonlinear siso processes with a neural model predictive control (nmpc) method. Computers & Chemical Engineering, 21, 201–210.CrossRefGoogle Scholar
  70. 70.
    Zhao, B., & Su, Y. (2010). Artificial neural network-based modeling of pressure drop coefficient for cyclone separators. Chemical Engineering Research and Design, 88(5–6), 606–613.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Carlos Mendes
    • 1
    Email author
  • Robson da Silva Magalhes
    • 1
  • Karla Esquerre
    • 1
  • Luciano Matos Queiroz
    • 2
  1. 1.Department of Chemical Engineering, Polytechnic SchoolFederal University of Bahia (UFBA)SalvadorBrazil
  2. 2.Department of Environmental Engineering, Polytechnic SchoolFederal University of Bahia (UFBA)SalvadorBrazil

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