Elucidating the performance of integrated anoxic/oxic moving bed biofilm reactor: Assessment of organics and nutrients removal and optimization using feed forward back propagation neural network

[Display omitted] •Carbon coated carriers and PU-foam frame in A/O MBBR enhanced biomass adhesion.•>95 % COD and ∼98 % NH4-N removals were observed at C/N ratios ≥ 6.75 and 3.5.•Maximum total nitrogen and PO43-- P removals were observed as 87.9% and 93%.•FF-BP-NN model was developed for accurate...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Bioresource technology 2023-03, Vol.371, p.128641-128641, Article 128641
Hauptverfasser: Saidulu, Duduku, Srivastava, Ashish, Gupta, Ashok Kumar
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:[Display omitted] •Carbon coated carriers and PU-foam frame in A/O MBBR enhanced biomass adhesion.•>95 % COD and ∼98 % NH4-N removals were observed at C/N ratios ≥ 6.75 and 3.5.•Maximum total nitrogen and PO43-- P removals were observed as 87.9% and 93%.•FF-BP-NN model was developed for accurate prediction of reactor performance.•Optimal operational conditions were evaluated using validated ANN model. A lab-scale integrated anoxic and oxic (A/O) moving bed biofilm reactor (MBBR) was investigated for the removal of organics and nutrients by varying chemical oxygen demand (COD) to NH4-N ratio (C/N ratio: 3.5, 6.75, and 10), hydraulic retention time (HRT: 6 h, 15 h, and 24 h), and recirculation ratio (R: 1, 2, and 3). The use of activated carbon coated carriers prepared from waste polyethylene material and polyurethane sponges attached to a cylindrical frame in the integrated A/O MBBR increased the attached growth biomass significantly. >95 % of COD removal was observed under the C/N ratio of 10 at an HRT of 24 h. While the low C/N ratio favored the removal of NH4-N (∼98 %) and PO43--P (∼90 %) with an optimal R of 1.75. Using the experimental dataset, to predict and forecast the performance of integrated A/O MBBR, a feed-forward-backpropagation-neural-network model was developed.
ISSN:0960-8524
1873-2976
DOI:10.1016/j.biortech.2023.128641