| Title | Gross parameters prediction of a granular-attached biomass reactor by means of multi-objective genetic-designed artificial neural networks: touristic pressure management case |
| Abstract | [object Object]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 R2
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 ANNMOGA
application with respect to a wide selection of scientific
literature cases. |
| Source | Environmental science and pollution research international |
| Keywords | Artificial neural networksFixed-bed bioreactorsPredictive modelsTouristic pressureWastewater treatment |
| Journal | Environmental science and pollution research international |
| Editor | Springer, Berlin, Germania |
| Year | 2015 |
| Type | Articolo in rivista |
| DOI | 10.1007/s11356-015-5729-3 |
| Authors | Del Moro G.; Barca E.; de Sanctis M.; Mascolo G.; Di Iaconi C. |
| Text | 340041 2015 10.1007/s11356 015 5729 3 Scopus 2 s2.0 84947087078 Artificial neural networks Fixed bed bioreactors Predictive models Touristic pressure Wastewater treatment 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. Istituto di Ricerca Sulle Acque, Consiglio Nazionale delle Ricerche, Viale F. De Blasio 5, Bari, 70132, Italy object Object 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 R2 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 ANNMOGA application with respect to a wide selection of scientific literature cases. Preprint http //www.scopus.com/inward/record.url eid=2 s2.0 84947087078 partnerID=q2rCbXpz Gross parameters prediction of a granular attached biomass reactor by means of multi objective genetic esigned artificial neural networks lavoro_reti_neurali.pdf Articolo in rivista Springer 0944 1344 Environmental science and pollution research international Environmental science and pollution research international Environ. sci. pollut. res. int. Environmental science and pollution research international. Environmental science and pollution research international Print Environmental science and pollution research Print ESPR Print claudio.diiaconi DI IACONI CLAUDIO emanuele.barca BARCA EMANUELE marco.desanctis DE SANCTIS MARCO guido.delmoro DEL MORO GUIDO giuseppe.mascolo MASCOLO GIUSEPPE |
