Multilayer feedforward networks are universal approximators
Neural Networks
Advances in neural information processing systems 2
New results on ensembles of multilayer feedforward
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Non-linear variable selection for artificial neural networks using partial mutual information
Environmental Modelling & Software
Modeling net ecosystem metabolism with an artificial neural network and Bayesian belief network
Environmental Modelling & Software
Review: Data-derived soft-sensors for biological wastewater treatment plants: An overview
Environmental Modelling & Software
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A laboratory-scale Activated Sludge System (ASS) was employed for the biodegradation of coke wastewater, which contains high concentrations of ammonium, thiocyanate, phenols and other organic compounds. The well-known kinetics models of Monod or Haldane are not very useful due to inhibition phenomena amongst the pollutants and also, they need the determination of a wide range of parameters to be introduced in the models. In this paper, a feed-forward neural network is outlined to obtain a satisfactory approach for estimating the effluent ammonium concentration of the treatment plant. The methodology consists in performing several tests with a group of different sizes of the hidden layer and different subsets of input variables. The developed model is useful to obtain simulations under different conditions of the influent stream, thus enabling the effluent ammonium concentration to be estimated. This neural network achieves better results than classical mathematical models for biological wastewater treatment as a result of the complex composition of the coke wastewater.