Impact of salinity on anaerobic ceramic membrane bioreactor for textile wastewater treatment: Process performance, membrane fouling and machine learning models

盐度 制浆造纸工业 陶瓷膜 织物 膜污染 膜生物反应器 废水 结垢 生物反应器 过程(计算) 废物管理 污水处理 化学 环境工程 环境科学 材料科学 生物 生态学 计算机科学 工程类 生物化学 复合材料 有机化学 操作系统
作者
Kanming Wang,Haoliang Zhang,Yuxiang Shen,Jiale Li,Wu Zhou,Hualong Song,Min Liu,Hongyu Wang
出处
期刊:Journal of Environmental Management [Elsevier BV]
卷期号:345: 118717-118717 被引量:14
标识
DOI:10.1016/j.jenvman.2023.118717
摘要

Anaerobic membrane bioreactor (AnMBR) shows great potential for textile wastewater treatment, but high salinity in the influent may undermine its performance. This study evaluated the impact of salinity on the treatment performance of an upflow anaerobic sludge blanket (UASB) configured AnMBR using a flat sheet ceramic membrane. The salinity was stepwise increased (0, 5, 10 and 20 g/L) in four phases of the AnMBR operation. Results indicated that increased salinity jeopardized the COD removal efficiency of AnMBR from 92% to 73%, but had a marginal effect on dye removal efficacy (90–96%). Low salinity (5 g/L) boosted the biogas production whilst high salinity (>10 g/L) had a negative impact. Additionally, the increase of salinity resulted in the soluble microbial production (SMP) concentration soar and membrane fouling rate increase, peaking at a salinity of 10 g/L (Phase III) and recovering back to a lower level at a salinity of 20 g/L (Phase IV). This indicated a transition occurrence at a salinity of 10 g/L (Phase III). The microbial diversity analyses further suggested a transition from salinity-sensitive microbes (Aminiphilus, Caldatribacterium, Mesotoga, Methanobrevibacter, Methanobacterium, Methanosaeta) to salinity-tolerant microbes (Longilinea, Ignavibacterium, Rhodovarius, Bosea and Flexilinea). This transition can be associated with the increase SMP concentration and more severe membrane fouling in Phase III, which were mitigated after a new equilibrium was reached when the microbial consortium acclimatized to the high salinity. Finally, a machine learning model of the Adaboost algorithm was established to predict COD removal under different salinities. Importantly, this study revealed that AnMBR process performance and membrane operation can be maintained for high salinity textile wastewater treatment with a halophilic microbial community growth under high-salinity selection pressure.
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