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


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.


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



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


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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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