Gross parameters prediction of a granular-attached biomass reactor by means of multi-objective genetic-designed artificial neural networks: touristic pressure management case

Del Moro, G.; Barca, E.; De Sanctis, M.; Mascolo, G.; Di Iaconi, C.

Gross parameters prediction of a granular-attached biomass reactor by means of multi-objective genetic-designed artificial neural networks: touristic pressure management case

2016

Del Moro, G. | Barca, E. | De Sanctis, M. | Mascolo, G. | Di Iaconi, C.

The Artificial Neural Networks by Multi-objective Genetic Algorithms (ANN-MOGA) model has been applied to gross parameters data of a Sequencing Batch Biofilter Granular Reactor (SBBGR) with the aim of providing an effective tool for predicting the fluctuations coming from touristic pressure. Six independent multivariate models, which were able to predict the dynamics of raw chemical oxygen demand (COD), soluble chemical oxygen demand (CODₛₒₗ), total suspended solid (TSS), total nitrogen (TN), ammoniacal nitrogen (N–NH₄⁺) and total phosphorus (Pₜₒₜ), were developed. The ANN-MOGA software application has shown to be suitable for addressing the SBBGR reactor modelling. The R ² found are very good, with values equal to 0.94, 0.92, 0.88, 0.88, 0.98 and 0.91 for COD, CODₛₒₗ, N–NH₄⁺, TN, Pₜₒₜ and TSS, respectively. A comparison was made between SBBGR and traditional activated sludge treatment plant modelling. The results showed the better performance of the ANN-MOGA application with respect to a wide selection of scientific literature cases.

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