Aerobic sludge granulation in shale gas flowback water treatment: Assessment of the bacterial community dynamics and modeling of bioreactor performance using artificial neural network.

Aerobic granular sludge Artificial neural network Bacterial community Flowback water

Journal

Bioresource technology
ISSN: 1873-2976
Titre abrégé: Bioresour Technol
Pays: England
ID NLM: 9889523

Informations de publication

Date de publication:
Oct 2020
Historique:
received: 05 05 2020
revised: 11 06 2020
accepted: 12 06 2020
pubmed: 24 6 2020
medline: 23 7 2020
entrez: 24 6 2020
Statut: ppublish

Résumé

Flowback water from shale gas extraction is highly saline and comprises complex organic substances, thereby posing a significant challenge for the environmental management of the unconventional natural gas industry. In this work, an aerobic granular sludge (AGS) method was successfully used for the treatment of flowback water from shale gas extraction. The formed AGS had a diameter of 0.25-2.0 mm and the total sludge volume index was 23.40 mL g

Identifiants

pubmed: 32574748
pii: S0960-8524(20)30959-7
doi: 10.1016/j.biortech.2020.123687
pii:
doi:

Substances chimiques

Natural Gas 0
Sewage 0
Waste Water 0
Water 059QF0KO0R

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

123687

Informations de copyright

Copyright © 2020 Elsevier Ltd. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Jiahao Liang (J)

State Key Laboratory of Heavy Oil Processing, State Key Laboratory of Petroleum Pollution Control, China University of Petroleum-Beijing, Beijing 102249, China.

Qinghong Wang (Q)

State Key Laboratory of Heavy Oil Processing, State Key Laboratory of Petroleum Pollution Control, China University of Petroleum-Beijing, Beijing 102249, China.

Qing X Li (QX)

Department of Molecular Biosciences and Bioengineering, University of Hawaii at Manoa, Honolulu, HI 96822, United States.

Liangyan Jiang (L)

State Key Laboratory of Heavy Oil Processing, State Key Laboratory of Petroleum Pollution Control, China University of Petroleum-Beijing, Beijing 102249, China.

Jiawen Kong (J)

State Key Laboratory of Heavy Oil Processing, State Key Laboratory of Petroleum Pollution Control, China University of Petroleum-Beijing, Beijing 102249, China.

Ming Ke (M)

State Key Laboratory of Heavy Oil Processing, State Key Laboratory of Petroleum Pollution Control, China University of Petroleum-Beijing, Beijing 102249, China.

Muhammad Arslan (M)

Department of Civil and Environmental Engineering, University of Alberta, Edmonton, Alberta T6G 1H9, Canada.

Mohamed Gamal El-Din (M)

Department of Civil and Environmental Engineering, University of Alberta, Edmonton, Alberta T6G 1H9, Canada.

Chunmao Chen (C)

State Key Laboratory of Heavy Oil Processing, State Key Laboratory of Petroleum Pollution Control, China University of Petroleum-Beijing, Beijing 102249, China. Electronic address: c.chen@cup.edu.cn.

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