膜科学与技术

位置：首页 >> 期刊内容阅读

基于人工神经网络的MBR膜污染研究现状

作者：樊吉霖¹，刘洪波¹，薛祝缘¹，王婧馨¹，王换男²，张睿思³

单位： 1.天津大学环境科学与工程学院，天津 300072 ；2.北京构力科技有限公司，北京100013；3.国家电投集团远达环保工程有限公司重庆科技分公司，重庆 401120

关键词：人工神经网络；MBR；膜污染

DOI号：

分类号： X703

出版年,卷(期):页码： 2021,41(4):154-159

摘要：

膜生物反应器（Membrane bioreactor，MBR）是膜分离技术与生物处理技术相结合的一种新型、高效的污水处理技术，当前膜污染已成为限制MBR工艺推广应用所面临的主要问题。本研究分析了当前MBR膜污染规律研究现状，指出了现有研究的不足，明确了人工神经网络（Artificial neural network，ANN）方法运用于MBR膜污染规律研究的优势；对ANN模型在MBR膜污染研究中的应用案例进行系统梳理，阐明了该方法在MBR膜污染研究领域的发展方向，为ANN模型在MBR膜污染领域的研究提供依据和参考。

MBRs are a new and efficient wastewater treatment method combining membrane separation and biological treatment technology. This study analyzed the current research status of the laws on membrane fouling in MBRs, identified the shortcomings of the existing research, and clarified the advantages of artificial neural networks (ANNs) in the study of the laws on membrane fouling in MBRs. The application cases of the ANN model in the research on membrane fouling of MBRs were systematically reviewed, and the developmental direction of this method in the research field of membrane fouling in MBRs was clarified, which provided a basis and reference for research with the ANN model in the field of membrane fouling in MBRs.

基金项目：

作者简介：

樊吉霖（1996.02），男，贵州毕节人，本科学历，市政工程硕士在读，研究方向:膜污染防治

参考文献：

[1] 韩红桂, 张硕, 乔俊飞. 基于递归RBF神经网络的MBR膜透水率软测量[J]. 北京工业大学学报, 2017, 43(08):1168-1174.
[2] Meng F, Zhang S, Oh Y, et al. Fouling in membrane bioreactors: An updated review[J]. Water Res, 2017, 114:151-180.
[3] 刘忠洲, 梁谷岩, 刘廷惠.膜材料与生物蛋白质相互作用的测定及膜污染的研究[J]. 环境科学丛刊, 1992(06):59-61.
[4] 唐朝春, 段先月, 叶鑫,等. MBR膜污染的机理及其影响因素研究进展[J]. 工业水处理, 2017, 37(04):18-21.
[5] Hamideh Hamedi, Majid Ehteshami, Seyed Ahmad Mirbagheri et al. New deterministic tools to systematically investigate fouling occurrence in membrane bioreactors[J]. Chem. Eng. Res. Des, 2019, 144:334-353.
[6] 马琳, 秦国彤. 膜污染的机理和数学模型研究进展[J] 水处理技术, 2007, 33(6):1-17

[7] Fortunato L, Li M, Cheng T, et al. Cake layer characterization in Activated Sludge Membrane Bioreactors: Real-time analysis[J]. J. Membr. Sci, 2019, 578:163-171.
[8] Hoek E, Agarwal G K. Extended DLVO interactions between spherical particles and rough surfaces[J]. J. Colloid Interface Sci, 2006, 298(1):50-58.
[9] Chen J, Mei R, Shen L, et al. Quantitative assessment of interfacial interactions with rough membrane surface and its implications for membrane selection and fabrication in a MBR[J]. Bioresour. Technol, 2015, 179: 367-372.
[10] van Oss C J. Acid—base interfacial interactions in aqueous media[J]. Colloids Surf, A, 1993, 78:1-49.
[11] Teng J, Shen L, He Y, et al. Novel insights into membrane fouling in a membrane bioreactor: Elucidating interfacial interactions with real membrane surface[J]. Chemosphere, 2018, 210:769-778.
[12] Alkmim A R, de Almeida G M, de Carvalho D M, et al. Improving knowledge about permeability in membrane bioreactors through sensitivity analysis using artificial neural networks[J]. Environ. Technol, 2020, 41(19):2424-2438.
[13] Lee J, Ahn W Y, Lee C H. Comparison of the filtration characteristics between attached and suspended growth microorganisms in submerged membrane bioreactor[J]. Water Res, 2001, 35(10):2435-2445.
[14] Wang P, Wang Z, Wu Z, et al. Effect of hypochlorite cleaning on the physiochemical characteristics of polyvinylidene fluoride membranes[J]. Chem. Eng. J, 2010, 162(3):1050-1056.
[15] Hajibabania S, Antony A, Leslie G, et al. Relative impact of fouling and cleaning on PVDF membrane hydraulic performances[J]. Sep. Purif. Technol, 2012, 90:204-212.
[16] Mirbagheri S A, Bagheri M, Bagheri Z, et al. Evaluation and prediction of membrane fouling in a submerged membrane bioreactor with simultaneous upward and downward aeration using artificial neural network-genetic algorithm[J]. Process Saf. Environ. Prot, 2015, 96:111-124.
[17] Park H, Lee Y H, Kim H, et al. Reduction of membrane fouling by simultaneous upward and downward air sparging in a pilot-scale submerged membrane bioreactor treating municipal wastewater[J]. Desalination, 2010, 251(1):75-82.
[18] 张硕. MBR污水处理膜污染智能预警方法研究[D]. 北京：北京工业大学, 2018.
[19] Geissler S, Wintgens T, Melin T, et al. Modelling approaches for filtration processes with novel submerged capillary modules in membrane bioreactors for wastewater treatment[J]. Desalination, 2005, 178(1):125-134.
[20] Aidan A, Abdel-Jabbar N, Ibrahim T H, et al. Neural network modeling and optimization of scheduling backwash for membrane bioreactor[J]. CLEAN TECHNOLOGIES AND ENVIRONMENTAL POLICY, 2008, 10(4):389-395.
[21] Xi X, Cui Y, Wang Z, et al. Study of dead-end microfiltration features in sequencing batch reactor (SBR) by optimized neural networks[J]. Desalination, 2011, 272(1-3):27-35.
[22] 刘志峰, 潘丹, 王建华,等. PSO-BP神经网络在MBR工艺中的膜污染预测[J]. 北京工业大学学报, 2012, 38(01):126-131.
[23] 闫宏英, 李春青. MBR膜污染的智能模拟预测方法研究[J]. 计算机测量与控制, 2013, 21(08): 2177-2180.
[24] 程孟刚, 彭贵芳, 廖竞萌. 基于MATLAB平台的RBF神经网络在膜分离废水中的应用[J]. 科技风, 2013(07):84.
[25] 胡文博, 李春青, 任淑霞. Adaboost-BP在MBR膜污染中的应用研究[J]. 软件, 2016, 37(12):21-25.
[26] 韩红桂, 张硕, 乔俊飞. 基于递归RBF神经网络的MBR膜透水率软测量[J]. 北京工业大学学报, 2017, 43(08):1168-1174.
[27] Zhao Z, Lou Y, Chen Y, et al. Prediction of interfacial interactions related with membrane fouling in a membrane bioreactor based on radial basis function artificial neural network (ANN)[J]. Bioresour. Technol, 2019, 282:262-268.
[28] Cai X, Shen L, Zhang M, et al. Membrane fouling in a submerged membrane bioreactor: An unified approach to construct topography and to evaluate interaction energy between two randomly rough surfaces[J]. Bioresour. Technol, 2017, 243:1121-1132.
[29] Chen Y, Teng J, Shen L, et al. Novel insights into membrane fouling caused by gel layer in a membrane bioreactor: Effects of hydrogen bonding[J]. Bioresour. Technol, 2019, 276:219-225.
[30] 陈镒锋, 申利国, 林红军. 人工神经网络在量化膜污染界面作用力中的应用[J]. 膜科学与技术, 2020, 40(3):47-54.

服务与反馈：

【文章下载】【加入收藏】

《膜科学与技术》编辑部地址：北京市朝阳区北三环东路19号蓝星大厦邮政编码：100029 电话：010-64426130/64433466 传真：010-80485372邮箱：mkxyjs@163.com