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

Mendes, Carlos; da Silva Magalhes, Robson; Esquerre, Karla; Queiroz, Luciano Matos

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

2015

Mendes, Carlos | da Silva Magalhes, Robson | Esquerre, Karla | Queiroz, Luciano Matos

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 ([Formula: see text]), and the root mean square error (RMSE). The comparison reveals that the DDM accurately selects the best model and reliably assesses its quality.

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