Aerobic sludge granulation in shale gas flowback water treatment: Assessment of the bacterial community dynamics and modeling of bioreactor performance using artificial neural network. (October 2020)

• Record Type: Journal Article
• Authors: Liang, Jiahao; Wang, Qinghong; Li, Qing X.; Jiang, Liangyan; Kong, Jiawen; Ke, Ming; Arslan, Muhammad; Gamal El-Din, Mohamed; Chen, Chunmao
• Is Part Of: Bioresource technology. Volume 313(2020)
• Publication Date: 2020-10
• Subjects: Flowback water -- Aerobic granular sludge -- Bacterial community -- Artificial neural network; Biomass -- Periodicals; Biomass energy -- Periodicals; Bioremediation -- Periodicals; Agricultural wastes -- Periodicals; Factory and trade waste -- Periodicals; Organic wastes -- Periodicals; Bioénergie -- Périodiques; Déchets agricoles -- Périodiques; Déchets industriels -- Périodiques; Déchets organiques -- Périodiques; Déchets (Combustible) -- Périodiques; 662.88
• DOI: 10.1016/j.biortech.2020.123687
• Languages: English
• ISSNs: 0960-8524

Abstract

Graphical abstract: Highlights: Aerobic granular sludge technology was used for industrial flowback water treatment. The granules exhibited high bacterial activity and excellent settleability. The aerobic granular sludge efficiently removed COD, NH4 + -N and TN. Nitrifying and denitrifying bacteria were enriched in aerobic granular sludge. ANN models successfully predicted removal efficiencies of COD, NH4 + -N and TN. Abstract: 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 −1 . The AGS efficiently removed COD, NH4 + -N and TN by 70.1%, 92.1%, and 59.2%, respectively. The bacterial communities responsible for the removal of nitrogen and degradation of organics were enriched in AGS. The dynamics of contaminant removal was further explained with a three-layered artificial neural network model. The results showed that the initial concentration of COD, TDS, NH4 + -N and TN governed the contaminants' removal. As for operating parameters, aerating time showed a strong effect on NH4 + -N and TN removal, whereas settling time impacted the COD removal.