A new substrate equalization method for optimizing the influent conditions and fluid flow patterns of a multifed upflow anaerobic sludge blanket reactor with mature anammox granules

Copyright © 2024 Elsevier Ltd. All rights reserved..

To improve nitrogen removal efficiency (NRE) and achieve homogenous distribution of anammox sludge and substrate, a new substrate equalization theory and a cumulative overload index was proposed for multifed upflow anaerobic sludge bed (MUASB) reactors with mature anammox granules. The performance and flow patterns of MUASB reactors were investigated under various influent conditions. The results showed that the nitrogen removal performance and stability of MUASB reactors could be optimized by minimizing the cumulative load. The NRE gradually increased from 83.3 ± 2.2 %, 86.8 ± 4.2 % to 89.3 ± 4.1 % and 89.7 ± 1.6 % in feeding flow tests and feeding port tests, respectively. Furthermore, the flow patterns were compared based on residence time distribution and computational fluid dynamics, indicating that a better equilibrium distribution of microorganisms and substrates could be achieved in the MUASB reactors under the lowest cumulative load. Therefore, substrate equalization theory can be used to optimize the nitrogen removal performance of MUASB reactors with low-carbon footprints.

Medienart:

E-Artikel

Erscheinungsjahr:

2024

Erschienen:

2024

Enthalten in:

Zur Gesamtaufnahme - volume:400

Enthalten in:

Bioresource technology - 400(2024) vom: 12. Apr., Seite 130700

Sprache:

Englisch

Beteiligte Personen:

Xing, Bao-Shan [VerfasserIn]
Tang, Xi-Fang [VerfasserIn]
Li, Ling-Hu [VerfasserIn]
Fu, Yu-Lin [VerfasserIn]
Liu, Jia-Yi [VerfasserIn]
Wang, Ya-Ge [VerfasserIn]
Sun, Xin-Xin [VerfasserIn]
Li, Yu-You [VerfasserIn]
Chen, Rong [VerfasserIn]
Jin, Ren-Cun [VerfasserIn]

Links:

Volltext

Themen:

Anaerobic granules
Computational fluid dynamics
Cumulative overload
Journal Article
Optimization strategy
Residence time distribution

Anmerkungen:

Date Revised 17.04.2024

published: Print-Electronic

Citation Status Publisher

doi:

10.1016/j.biortech.2024.130700

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM371052890