Identification and Suppression of Abnormal Conditions in Municipal Wastewater Treatment Process
-
摘要: 城市污水处理过程（Municipal wastewater treatment processes，WWTPs）由于进水流量、进水成分、污染物种类、有机物浓度等被动接受，系统始终运行在非平稳状态，导致污泥膨胀等异常工况频发.异常工况一旦发生，会降低污水处理效率，引起出水水质超标等问题，严重时造成污水处理过程崩溃，引发事故.因此，如何降低异常工况发生率、保证城市污水处理过程安全平稳运行，是城市污水处理过程亟待解决的难题.围绕城市污水处理过程异常工况的识别和抑制方法，文中梳理了其研究进展.首先，介绍了城市污水处理运行的背景与异常工况的特点；其次，概述了一些主流的污水处理异常工况识别和抑制方法；最后，进行了分析与总结，指出了城市污水处理过程异常工况识别和抑制方法未来的研究方向.Abstract: Since the influent flux, components, contamination's variety and concentration of municipal wastewater treatment processes (WWTPs) is passive acceptance, the operation of municipal WWTPs always stay in the non-stationary state, which frequently leads to terrible abnormal conditions, such as sludge bulking. Once abnormal conditions happen, it will decrease the efficiency of municipal WWTPs and bring the problem of unqualified effluent. In serious cases, the collapse of municipal WWTPs and even accidents are caused. Thus, it is also a crucial task to reduce the occurrence of abnormal conditions and ensure security and stability of municipal WWTPs. In this paper, the researches with respect to identification and suppression of abnormal conditions in municipal WWTPs are reviewed. First, we introduce the background of municipal WWTPs and characteristics of abnormal conditions. Second, the overview of methods that identify and suppress the abnormal conditions is presented. Finally, we realize the analysis and summary of this paper, and point out the future research direction of identification and suppression methods for abnormal conditions in municipal WWTPs.1) 本文责任编委 孙健
-
图 1 污水处理异常
Fig. 1 The data-driven method of abnormal conditions for wastewater treatment plant
图 2 污水处理异常工况的抑制方法
Fig. 2 The suppression method of abnormal conditions for wastewater treatment plant
-
[1] Wang Z W. China's wastewater treatment goals. Science, 2012, 338(2):604 http://d.old.wanfangdata.com.cn/NSTLQK/NSTL_QKJJ0228538092/ [2] Loosdrecht M C M Van, Brdjanovic D. Anticipating the next century of wastewater treatment. Science, 2014, 344(6):1452-1453 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=18ea732cfbb1dd85c2959b83ab0e3e10 [3] 李激, 郑凯凯, 王燕, 施汉昌.智能化城市污水处理厂运行专家系统的研究.中国给水排水, 2016, 32(11):1-5 http://www.cnki.com.cn/Article/CJFDTOTAL-GSPS201611004.htmLi Ji, Zheng Kai-Kai, Wang Yan, Shi Han-Chang. Intelligent operation expert system for municipal wastewater treatment plant. China Water & Wastewater, 2016, 32(11):1-5 http://www.cnki.com.cn/Article/CJFDTOTAL-GSPS201611004.htm [4] Gustaf O. ICA and me-a subjective review. Water Research, 2012, 46(1):7-15 http://d.old.wanfangdata.com.cn/NSTLQK/NSTL_QKJJ0226525066/ [5] 徐天凯, 彭党聪, 徐涛, 李惠娟, 姚倩, 金虎.城市污水处理厂A2/O工艺污泥膨胀与上浮的诊断.中国给水排水, 2016, 32(23):31-35 http://www.cnki.com.cn/Article/CJFDTOTAL-GSPS201623009.htmXu Tian-Kai, Peng Dang-Cong, Xu Tao, Li Hui-Juan, Yao Qian, Jin Hu. Diagnosis of sludge bulking and floating in A2/O process in municipal wastewater treatment plant. China Water & Wastewater, 2016, 32(23):31-35 http://www.cnki.com.cn/Article/CJFDTOTAL-GSPS201623009.htm [6] Breach P A, Simonovic S P. Wastewater treatment energy recovery potential for adaptation to global change:an integrated assessment. Environmental Management, 2018, 61(4):1-13 http://europepmc.org/abstract/MED/29423714 [7] Tian W D, Li W Gg, Zhang H, Kang X R, Mark C M L. Limited filamentous bulking in order to enhance integrated nutrient removal and effluent quality. Water Research, 2011, 45(16):4877-4884 doi: 10.1016/j.watres.2011.06.034 [8] Khalida M, Azmi A, Mohd R S, Zaharah I, Adibah Y, Mark C M L, et al. Development of granular sludge for textile wastewater treatment. Water Research, 2010, 44(15):4341-4350 doi: 10.1016/j.watres.2010.05.023 [9] Antonio M P M, Krishna P, Joseph J H, Mark C M L. Filamentous bulking sludge-a critical review. Water Research, 2004, 38(4):793-817 doi: 10.1016/j.watres.2003.11.005 [10] Ni B J, Yu H Q. Mathematical modeling of aerobic granular sludge:a review. Biotechnology Advances, 2010, 28(6):895-909 doi: 10.1016/j.biotechadv.2010.08.004 [11] Wang J, Li Q, Qi R, Tan V, Yang M. Sludge bulking impact on relevant bacterial populations in a full-scale municipal wastewater treatment plant. Process Biochemistry, 2014, 49(12):2258-2265 doi: 10.1016/j.procbio.2014.08.005 [12] 谢冰, 徐亚同.活性污泥污水处理厂生物泡沫产生机理及控制.净水技术, 2006, 25(1):1-6 doi: 10.3969/j.issn.1009-0177.2006.01.001Xie Bing, Xu Ya-Tong. Review of the mechanism and control of scum and foaming for sewage treatment plant. Water Purification Technology, 2006, 25(1):1-6 doi: 10.3969/j.issn.1009-0177.2006.01.001 [13] 李宗仁, 张新颖, 林琳琳, 张莉敏, 杜朝丹, 陈美香.污泥膨胀和生物泡沫的形成机理和控制方法.净水技术, 2018, 37(4):28-33 http://d.old.wanfangdata.com.cn/Periodical/jsjs201804005Li Zong-Ren, Zhang Xin-Ying, Lin Lin-Lin, Zhang Li-Min, Du Chao-Dan, Chen Mei-Xiang. Mechanism and control solutions for formation of sludge bulking and biological foaming. Water Purification Technology, 2018, 37(4):28-33 http://d.old.wanfangdata.com.cn/Periodical/jsjs201804005 [14] Wang J B, Chai L. H, Zhang Y, Chen L M. Microbial ecological model of filamentous bulking and mechanisms. World Microbiol Biotechnol, 2006, 22(10):1313-1320 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=8026add19fec2c0c713c6d9d9e82a2e2 [15] Guo J H, Peng Y Z, Peng C Y, Wang S Y, Chen Y, Huang H J, et al. Energy saving achieved by limited filamentous bulking under low dissolved oxygen. Bioresource Technology, 2008, 29(12):3342-3347 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=4fd1a5e34b6abee082064fde055ded91 [16] Amaral A L, Ferreira E C. Activated sludge monitoring of a wastewater treatment plant using image analysis and partial least squares regression. Analytica Chimica Acta, 2005, 544(1):246-253 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=ff37adbddba05307243e1ea3d2dd82ea [17] Reyes D L, Raskin L. Role of filamentous microorganisms in activated sludge foaming:relationship of mycolata levels to foaming initiation and stability. Water Research, 2002, 36(2):445-459 doi: 10.1016/S0043-1354(01)00227-5 [18] Fryer M, Gray N F. Foaming scum index (FSI)-a new tool for the assessment and characterization of biological mediated activated sludge foams. Journal of Environmental Management, 2012, 110(18):8-19 http://www.sciencedirect.com/science/article/pii/S0301479712002678 [19] Frigon D, Guthrie R M, Bachman G T, Royer J, Bailey B, Raskin L. Long-term analysis of a full-scale activated sludge wastewater treatment system exhibiting seasonal biological foaming. Water Research, 2006, 40(5):990-1008 doi: 10.1016/j.watres.2005.12.015 [20] Di B G, Torregrossa M. Foaming in membrane bioreactors:Identification of the causes. Journal of Environmental Management, 2013, 128(20):453-461 [21] Rika J, Ephraim N B, Jeron D, Nele R J, Jan F V I. Detection of filamentous bulking problems:developing an image analysis system for sludge composition monitoring. Microsc. Microanal, 2007, 13(1):36-41 doi: 10.1017/S1431927607070092 [22] Fujihira T, Seo S, Yamaguchi T, Hatamoto M, Tanikawa D. High-rate anaerobic treatment system for solid/lipid-rich wastewater using anaerobic baffled reactor with scum recovery. Bioresource Technology, 2018, 263(1):145-152 http://europepmc.org/abstract/MED/29738977 [23] Tsang Y F, Chua H, Sin S N, Tam C Y. A novel technology for bulking control in biological wastewater treatment plant for pulp and paper making industry. Biochemical Engineering Journal, 2006, 32(3):127-134 doi: 10.1016/j.bej.2006.08.014 [24] Han H G, Liu Z, Guo Y N, Qiao J F. An intelligent detection method for bulking sludge of wastewater treatment process. Journal of Process Control. Journal of Process Control, 2018, 68(8):118-128 http://www.sciencedirect.com/science/article/pii/S095915241830074X [25] Brault J M, Labib R, Perrier M, Stuart P. Prediction of activated sludge filamentous bulking using ATP DATA and neural networks. The Canadian Journal of Chemical Engineering, 2011, 89(4):901-913 doi: 10.1002/cjce.v89.4 [26] Han H G, Qiao J F. Prediction of activated sludge bulking based on a self-organizing RBF neural network. Joural of Process Control, 2012, 22(6):1103-1112 doi: 10.1016/j.jprocont.2012.04.002 [27] 崔和平, 钟艳萍.丝状菌污泥膨胀的原因及其控制方法.中国给水排水, 2004, 20(6):99-101 doi: 10.3321/j.issn:1000-4602.2004.06.032Cui He-Ping, Zhong Yan-Ping. Causes and control of filamentous sludge bulking. China Water & Wastewater, 2004, 20(6):99-101 doi: 10.3321/j.issn:1000-4602.2004.06.032 [28] Liu Y, Liu Q S. Causes and control of filamentous growth in aerobic granular sludge sequencing batch reactors. Biotechnology Advances, 2006, 24(1):115-127 doi: 10.1016/j.biotechadv.2005.08.001 [29] Han H G, Li Y, Qiao J F. A fuzzy neural network approach for online fault detection in waste water treatment process. Computers & Electrical Engineering, 2014, 40(7):2216-2226 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=3fcb51960a970d369cf416a8346e77b5 [30] Guo F, Zhang T. Profiling bulking and foaming bacteria in activated sludge by high throughput sequencing. Water Research, 2012, 46(8):2772-2782 doi: 10.1016/j.watres.2012.02.039 [31] Zhou L, Peng Y Z, Li L Y, Ma T. Study on the recovery of settleability of low DO filamentous bulking sludge. Industrial Water Treatment, 2006, 26(10):49-51 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=gyscl200610015 [32] Han H G, Li M, Qiao J F. Design of dynamic RBF neural network based on the sensitivity analysis of model output. Acta Electronica Sinica, 2010, 38(3):731-736 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=xxykz200903021 [33] Jose R V P, Anuska M C, Jose L C, Ramon M, Julian C, Julio P. Modeling aerobic granular SBR at variable COD/N ratios including accurate description of total solids concentration. Biochemical Engineering Journal, 2012, 49(2):173-184 [34] Mogens H, Willi G, Takahashi M, Mark C M V L. Actived Sludge Models ASM1, ASM2, ASM2d, ASM3. IWA Pulishing, 2000 [35] David A M, Oscar F M, Nadia K, Farah D H D. A review of recent developments in modeling of microbial growth kinetics and intraparticle phenomena in solid-state fermentation. Biochemical Engineering Journal, 2004, 17(1):15-26 doi: 10.1016/S1369-703X(03)00120-7 [36] Irene J, Julian C, Javier L, Juan A B. Start-up of a nitrification system with automatic control to treat highly concentrated ammonium wastewater:Experimental results and modeling. Chemical Engineering Journal, 2008, 144(3):407-419 doi: 10.1016/j.cej.2008.02.010 [37] Ng W J, Ong S L, Faisal H. An algorithmic approach for system-specific modeling of activated sludge bulking in an SBR. Environmental Modelling & Software, 2000, 15(2):199-210 doi: 10.1016-S1364-8152(99)00035-3/ [38] Vaiopoulou E, Melidis P, Aivasidis A. Growth of filamentous bacteria in an enhanced biological phosphorus removal system. Desalination, 2007, 213(1):288-296 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=ce8a93e8ff9c4a9d3064b07a24ba77f4 [39] Guo J, Peng Y, Wang S. Stable limited filamentous bulking through keeping the competition between floc-formers and filaments in balance. Bioresource Technology, 2012, 103(1):7-15 doi: 10.1016/j.biortech.2011.08.114 [40] Yu Y, Qiao J F. Modeling and simulation technology of activated sludge method on wastewater treatment process. Information and Control, 2004, 9(6):22-34 http://en.cnki.com.cn/Article_en/CJFDTOTAL-XXYK200406014.htm [41] Kiser M A, Westerhoff P, Benn T. Titanium nanomaterial removal and release from wastewater treatment plants. Environmental Science & Technology, 2009, 43(17):6757-6763 http://d.old.wanfangdata.com.cn/NSTLQK/NSTL_QKJJ0211268444/ [42] Bagheri M, Mirbagheri S A, Bagheri Z. Modeling and optimization of activated sludge bulking for a real wastewater treatment plant using hybrid artificial neural networks-genetic algorithm approach. Process Safety & Environmental Protection, 2015, 95(1):12-25 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=75e9c999ba938bf69edd80db06a8d94d [43] Martins A M P, Karahan O, Loosdrecht M C M. Effect of polymeric substrate on sludge settleability. Water Research, 2011, 45(1):263-273 doi: 10.1016/j.watres.2010.07.055 [44] Kim H, Gellner J W, Boltz J P. Effects of integrated fixed film activated sludge media on activated sludge settling in biological nutrient removal systems. Water Research, 2010, 44(5):1553-1561 doi: 10.1016/j.watres.2009.11.001 [45] Hu B, Qi R, An W. Dynamics of the microfauna community in a full-scale municipal wastewater treatment plant experiencing sludge bulking. European Journal of Protistology, 2013, 49(4):491-499 doi: 10.1016/j.ejop.2013.03.001 [46] Tixier N, Guibaud G, Baudu M. Towards a rheological parameter for activated sludge bulking characterisation. Enzyme & Microbial Technology, 2003, 33(2):292-298 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=81cac6ef40c73acb45ce2fb20399ebf4 [47] Motta M, Pons M N, Roche N. Automated monitoring of activated sludge in a pilot plant using image analysis. Water Science & Technology, 2001, 47(7):91-96 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=7d9435854f43dea60fd474c039147e9c [48] Mamais D, Andreadakis A, Noutsopoulous C, Kalergis C. Causes of, and control strategies for, microthrix parvicella bulking and foaming in nutrient removal activated sludge systems. Water Science & Technology, 1998, 37(4-5):9-17 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=569896202df0d8781765f0593ea5c335 [49] Banadda E N, Smets I Y, Jenne R, Van I J F. Predicting the onset of filamentous bulking in biological wastewater treatment systems by exploiting image analysis information. Bioprocess & Biosystems Engineering, 2005, 27(5):339-348 http://lirias.kuleuven.be/handle/123456789/212720 [50] Boztoprak H, Ozbay Y, Guclu D, Kucukhemek M. Prediction of sludge volume index bulking using image analysis and neural network at a full-scale activated sludge plant. Desalination & Water Treatment, 2015, 57(37):1-11 doi: 10.1080/19443994.2015.1085909 [51] Nilsson F, Hagman M, Mielczarek A T, Nielsen P H, Nsson K. Application of ozone in full-scale to reduce filamentous bulking sludge at Aresundsverket WWTP. Ozone Science & Engineering, 2014, 36(3):238-243 http://www.mendeley.com/research/application-ozone-fullscale-reduce-filamentous-bulking-sludge-%E6%9E%9Aresundsverket-wwtp/ [52] Wang P, Yu Z, Qi R, Zhang H. Detailed comparison of bacterial communities during seasonal sludge bulking in a municipal wastewater treatment plant. Water Research, 2016, 105(1):157-163 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=508147f060dfc9b9524249785274fed1 [53] Mesquita D P, Dias O, Dias A M, Amaral A L, Ferreira E C. Correlation between sludge settling ability and image analysis information using partial least squares. Analytica Chimica Acta, 2009, 642(1):94-101 http://d.old.wanfangdata.com.cn/NSTLQK/NSTL_QKJJ0215214381/ [54] Liu Y, Wang Z W, Liu Y Q. A generalized model for settling velocity of aerobic granular sludge. Biotechnology Progress, 2005, 21(2):621-626 http://www.ncbi.nlm.nih.gov/pubmed/15801809 [55] Ratkovich N, Horn W, Helmus F P. Activated sludge rheology:a critical review on data collection and modelling. Water Research, 2013, 47(2):463-482 doi: 10.1016/j.watres.2012.11.021 [56] Heine W, Sekoulov I, Burkhardt H. Early warning-system for operation-failures in biological stages of WWTPs by on-line image analysis. Water Science & Technology, 2002, 46(4-5):117-124 http://www.ncbi.nlm.nih.gov/pubmed/12360998 [57] Haimi H, Mulas M, Corona F, Vahala R. Data-derived soft-sensors for biological wastewater treatment plants:an overview. Environmental Modelling & Software, 2013, 47(3):88-107 http://www.sciencedirect.com/science/article/pii/S1364815213001308 [58] Makinia J, Rosenwinkel K H, Phan L C. Modification of ASM3 for the determination of biomass adsorption/storage capacity in bulking sludge control. Water Science & Technology, 2006, 53(3):91-98 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=df51a3891cef0c4f16d9ae9bbb4a0d36 [59] Bansal N K, Feng X, Zhang W, Wei W, Zhao Y. Modeling temporal pattern and event detection using hidden Markov model with application to a sludge bulking data. Procedia Computer Science, 2012, 12(1):218-223 http://www.sciencedirect.com/science/article/pii/S1877050912006503 [60] Xavier F A, Joaquim C, Ignasi R R, Krist V G, Christian R. Including the effects of filamentous bulking sludge during the simulation of wastewater treatment plants using a risk assessment model. Water Research, 2009, 43(18):4527-4538 doi: 10.1016/j.watres.2009.07.033 [61] Mesquita D P, Amaral A L, Ferreira E C. Activated sludge characterization through microscopy:a review on quantitative image analysis and chemometric techniques. Analytica Chimica Acta, 2013, 802(1):14-28 http://www.ncbi.nlm.nih.gov/pubmed/24176501 [62] Smets I, Banadda E, Deurinck J, Renders N, Jenne R, Van I J. Dynamic modeling of filamentous bulking in lab-scale activated sludge processes. Journal of Process Control, 2006, 16(3):313-319 doi: 10.1016/j.jprocont.2005.06.011 [63] 韩红桂, 伍小龙, 王丽丹, 王思.丝状菌污泥膨胀简化机理模型研究.化工学报, 2013, 64(12):4641-4648 http://www.cnki.com.cn/Article/CJFDTOTAL-HGSZ201312055.htmHan Hong-Gui, Wu Xiao-Long, Wang Li-Dan, Wang Si. The analysis of the mechanistic model of filamentous bulking. Chinese Journal of Chemical Engineering, 2013, 64(12):4641-4648 http://www.cnki.com.cn/Article/CJFDTOTAL-HGSZ201312055.htm [64] Zhang C, Zhang H. Analysis of aerobic granular sludge formation based on grey system theory. Journal of Environmental Sciences, 2013, 25(4):710-716 doi: 10.1016/S1001-0742(12)60080-1 [65] Ng W J, Ong S L, Hossain F. An algorithmic approach for system-specific modelling of activated sludge bulking in an SBR. Environmental Modelling & Software, 2000, 15(2):199-210 https://www.sciencedirect.com/science/article/pii/S1364815299000353 [66] Comas J, Rodríguez-Roda I, Gernaey K V, Rosen C, Jeppsson U. Risk assessment modelling of microbiology-related solids separation problems in activated sludge systems. Environmental Modelling & Software, 2008, 32(10):1250-1261 [67] Capodaglio A G, Jones H V, Novotny V, Feng X. Sludge bulking analysis and forecasting:application of system identification and artificial neural computing technologies. Water Research, 1991, 25(10):1217-1224 doi: 10.1016/0043-1354(91)90060-4 [68] Lou I, Zhao Y. Sludge bulking prediction using principle component regression and artificial neural network. Mathematical Problems in Engineering, 2012, 25(10):295-308 http://d.old.wanfangdata.com.cn/NSTLQK/NSTL_QKJJ0230611658/ [69] Barnett M W. Knowledge-based expert system applications in waste treatment operation and control. ISA Transactions, 1992, 31(1):53-60 doi: 10.1016/0019-0578(92)90009-8 [70] Traore A, Grieu S, Thiery F. Control of sludge height in a secondary settler using fuzzy algorithms. Computers & Chemical Engineering, 2006, 30(8):1235-1242 http://www.sciencedirect.com/science/article/pii/S0098135406000421 [71] Fialkowska E, Pajdakstos A. The role of Lecane rotifers in activated sludge bulking control. Water Research, 2008, 42(10-11):2483-2490 doi: 10.1016/j.watres.2008.02.001 [72] Ni B J, Yu H Q, Sun Y J. Modeling simultaneous autotrophic and heterotrophic growth in aerobic granules. Water Research, 2008, 42(6):1583-1594 doi: 10.1016-j.watres.2007.11.010/ [73] Vazquez P J R, Mosquera C A, Campos J L. Modelling aerobic granular SBR at variable COD/N ratios including accurate description of total solids concentration. Biochemical Engineering Journal, 2010, 49(2):173-184 doi: 10.1016/j.bej.2009.12.009 [74] Rossle W H, Pretorius W A. Batch and automated SVI measurements based on short-term temperature variations. Water SA, 2008, 34(2):237-243 [75] Kotay S M, Datta T, Choi J, Goel R. Biocontrol of biomass bulking caused by Haliscomenobacter hydrossis using a newly isolated lytic bacteriophage. Water Research, 2011, 45(2):694-704 doi: 10.1016/j.watres.2010.08.038 [76] Seka A M, Wiele T V D, Verstraete W. Feasibility of a multi-component additive for efficient control of activated sludge filamentous bulking. Water Research, 2001, 35(12):2995-3003 doi: 10.1016/S0043-1354(00)00589-3 [77] Dierdonck J V, Broeck R V D, Vervoort E. The effect of alternating influent carbon source composition on activated sludge bioflocculation. Journal of Biotechnology, 2013, 167(3):225-234 doi: 10.1016/j.jbiotec.2013.07.012 [78] Lyko S, Teichgraber B, Kraft A. Bulking control by low-dose ozonation of returned activated sludge in a full-scale wastewater treatment plant. Water Science & Technology, 2012, 65(9):1654-1659 http://www.ncbi.nlm.nih.gov/pubmed/22508129 [79] Barrington D J, Ghadouani A. Application of hydrogen peroxide for the removal of toxic cyanobacteria and other phytoplankton from wastewater. Environmental Science & Technology, 2008, 42(23):8916-8921 doi: 10.1021/es801717y [80] Hartley K J. Controlling sludge settleability in the oxidation ditch process. Water Research, 2008, 42(6-7):1459-1466 doi: 10.1016/j.watres.2007.10.017 [81] Nilsson F, Davidsson A, Falas P, Bengtsson S, Kai B, Karin J. Impact of activated sludge ozonation on filamentous bacteria viability and possible added benefits. Environmental Technology, 2018, 4:1-7 doi: 10.1080/09593330.2018.1447023 [82] Pitman A R. Bulking and foaming in bnr plants in Johannesburg:Problems and solution. Water Science & Technology, 1996, 34(3-4):291-298 http://www.sciencedirect.com/science/article/pii/0273122396005859 [83] 李宝新, 金波.污水处理厂生物泡沫的预防和控制方法.工业水处理, 2010, 30(2):81-83 doi: 10.3969/j.issn.1005-829X.2010.02.025Li Bao-Xin, Jin Bo. Prevention and control measures of biobubbles in wastewater treatment plants. Industrial Water Treatment, 2010, 30(2):81-83 doi: 10.3969/j.issn.1005-829X.2010.02.025 [84] Levacn L, Wijnbladh E, Tuvesson M, Kragelund C, Hallin S. Control of Microthrix parvicella and sludge bulking by ozone in a full-scale WWTP. Water Science & Technology, 2016, 73(4):866-872 http://www.ncbi.nlm.nih.gov/pubmed/26901730 [85] Parker D, Appleton R, Bratby J. North American performance experience with anoxic and anaerobic selectors for activated sludge bulking control. Water Science & Technology, 2004, 50(7):221-228 http://test.europepmc.org/abstract/MED/15553479 [86] Jiang M, Zhang Y, Zhou X. Simultaneous carbon and nutrient removal in an airlift loop reactor under a limited filamentous bulking state. Bioresource Technology, 2013, 130(1):406-411 http://www.ncbi.nlm.nih.gov/pubmed/23313686 [87] 赵霞, 赵阳丽, 陈忠林.好氧颗粒污泥发生丝状菌污泥膨胀的控制措施.中国给水排水, 2012, 28(3):15-19 doi: 10.3969/j.issn.1000-4602.2012.03.004Zhao Xia, Zhao Yang-Li, Chen Zhong-Lin. Control of filamentous sludge bulking in aerobic granular sludge SBR process. China Water & Wastewater, 2012, 28(3):15-19 doi: 10.3969/j.issn.1000-4602.2012.03.004 [88] Meunier C, Henriet O, Schoonbroodt B. Influence of feeding pattern and hydraulic selection pressure to control filamentous bulking in biological treatment of dairy wastewaters. Bioresource Technology, 2016, 221(9):300-309 http://www.ncbi.nlm.nih.gov/pubmed/27643739 [89] Schuler A J, Jassby D. Filament content threshold for activated sludge bulking:Artifact or reality? Water Research, 2007, 41(19):4349-4356 doi: 10.1016/j.watres.2007.06.021 [90] 周利, 彭永臻, 高春娣, 丁峰. SBR工艺中污泥负荷对丝状菌污泥膨胀的影响.中国给水排水, 1999, 15(6):11-13 doi: 10.3321/j.issn:1000-4602.1999.06.004Zhou Li, Peng Yong-Zhen, Gao Chun-Di, Ding Feng. Influence of sludge load on filamentous sludge in SBR process. China Water & Wastewater, 1999, 15(6):11-13 doi: 10.3321/j.issn:1000-4602.1999.06.004 [91] Song X L, Zhao Y B, Song Z Y. Dissolved oxygen control in wastewater treatment based on robust PID controller. International Journal of Modelling Identification & Control, 2012, 15(4):297-303 http://www.ingentaconnect.com/content/rsoc/17466172/2012/00000015/00000004/art00009 [92] Cristea M V, Agachi S P. Nonlinear model predictive control of the wastewater treatment plant. Computer Aided Chemical Engineering, 2006, 21(1):1365-1370 http://www.sciencedirect.com/science/article/pii/S1570794606802373 [93] Corriou J P, Pons M N. Model predictive control of wastewater treatment plants:Application to the BSM1. Computer Aided Chemical Engineering, 2004, 32(4):625-630 doi: 10.1016-S1570-7946(04)80170-6/ [94] Han H G, Qiao J F. Nonlinear model-predictive control for industrial processes:an application to wastewater treatment process. IEEE Transactions on Industrial Electronics, 2014, 61(4):1970-1982 doi: 10.1109/TIE.2013.2266086 [95] Francisco M, Skogestad S, Vega P. Model predictive control for the self-optimized operation in wastewater treatment plants:Analysis of dynamic issues. Computers & Chemical Engineering, 2015, 82(3):259-272 http://europepmc.org/articles/PMC4344236/ [96] Guo J H, Peng Y Z, Peng C Y. Energy saving achieved by limited filamentous bulking sludge under low dissolved oxygen. Bioresource Technology, 2010, 101(4):1120-1126 doi: 10.1016/j.biortech.2009.09.051 [97] Amanatidou E, Samiotis G, Trikoilidou E. Evaluating sedimentation problems in activated sludge treatment plants operating at complete sludge retention time. Water Research, 2015, 69(1):20-29 http://www.ncbi.nlm.nih.gov/pubmed/25463928 [98] 栗三一, 乔俊飞, 李文静, 顾锞.污水处理决策优化控制.自动化学报, 2018, 44(12):2198-2209 http://youxian.cnki.com.cn/yxdetail.aspx?filename=MOTO2017121102J&dbname=CAPJ2015Li San-Yi, Qiao Jun-Fei, Li Wen-Jing, Gu Ke. Advanced decision and optimization control system for wastewater treatment plants. Acta Automatica Sinica, 2018, 44(12):2198-2209 http://youxian.cnki.com.cn/yxdetail.aspx?filename=MOTO2017121102J&dbname=CAPJ2015 [99] Krzeminski P, Iglesias-Obelleiro A, Madebo G. Impact of temperature on raw wastewater composition and activated sludge filterability in full-scale MBR systems for municipal sewage treatment. Journal of Membrane Science, 2012, 423(1):348-361 http://dl.acm.org/citation.cfm?id=2455338 [100] Han H G, Wu X L, Liu Z, Qiao J F. Design of self-organizing intelligent controller using fuzzy neural network. IEEE Transactions on Fuzzy Systems, 2018, 26(5):3097-3111 doi: 10.1109/TFUZZ.2017.2785812 [101] Zeng G, Jiang R, Huang G, Xu M, Li J. Optimization of wastewater treatment alternative selection by hierarchy grey relational analysis. IEEE Transactions on Fuzzy Systems, 2018, 26(5):3097-3111 http://www.sciencedirect.com/science/article/pii/S0301479706000594 [102] Ruan J, Chao Z, Li Y. Improving the efficiency of dissolved oxygen control using an on-line control system based on a genetic algorithm evolving FWNN software sensor. Journal of Environmental Management, 2017, 187(1):550-559 http://www.sciencedirect.com/science/article/pii/S0301479716308581 [103] Ramin E, Sin G, Mikkelsen P S. Significance of settling model structures and parameter subsets in modelling WWTPs under wet-weather flow and filamentous bulking conditions. Water Research, 2014, 63(1):209-221 http://europepmc.org/abstract/med/25003213 [104] Avella A C, Gorner T, Yvon J. A combined approach for a better understanding of wastewater treatment plants operation:Statistical analysis of monitoring database and sludge physico-chemical characterization. Water Research, 2011, 45(3):981-992 doi: 10.1016/j.watres.2010.09.028 [105] Ding D, Feng C, Jin Y. Domestic sewage treatment in a sequencing batch biofilm reactor (SBBR) with an intelligent controlling system. Desalination, 2011, 276(1):260-265 http://www.sciencedirect.com/science/article/pii/S0011916411002773 [106] Belchior C A C, Rui A M A, Landeck J A C. Dissolved oxygen control of the activated sludge wastewater treatment process using stable adaptive fuzzy control. Computers & Chemical Engineering, 2012, 37(4):152-162 http://www.sciencedirect.com/science/article/pii/S0098135411002821 [107] Leng G, McGinnity T M, Prasad G. Design for self-organizing fuzzy neural networks based on genetic algorithms. IEEE Transactions on Fuzzy Systems, 2006, 14(6):755-766 doi: 10.1109/TFUZZ.2006.877361 [108] Juang C F, Chiu S H, Chang S W. A self-organizing TS-type fuzzy network with support vector learning and its application to classification problems. IEEE Transactions on Fuzzy Systems, 2007, 15(5):998-1008 doi: 10.1109/TFUZZ.2007.894980 [109] Han H G, Qiao J F. A self-organizing fuzzy neural network based on a growing-and-pruning algorithm. IEEE Transactions on Fuzzy Systems, 2010, 18(6):1129-1143 doi: 10.1109/TFUZZ.2010.2070841 [110] Pocha M, Comasa J, Porroa J. Where are we in wastewater treatment plants data management? A review and a proposal. International Environmental Modelling & Software Society, 2014, 12(3):221-234 [111] Rieger L, Takács I, Villez K. Data reconciliation for wastewater treatment plant simulation studies-planning for high-quality data and typical sources of errors. Water Environment Research, 2010, 82(5):426-433 doi: 10.2175/106143009X12529484815511 [112] Puig S, Loosdrecht M C M, Colprim J. Data evaluation of full-scale wastewater treatment plants by mass balance. Water Research, 2008, 42(18):4645-4655 doi: 10.1016/j.watres.2008.08.009 [113] Amaral A L, Mesquita D P, Ferreira E C. Automatic identification of activated sludge disturbances and assessment of operational parameters. Chemosphere, 2013, 91(5):705-710 doi: 10.1016/j.chemosphere.2012.12.066 [114] Castillo A, Cheali P, Gomez V, Comas J, Poch M, Sin G. An integrated knowledge-based and optimization tool for the sustainable selection of wastewater treatment process concepts. Environmental Modelling & Software, 2016, 84:177-192 http://www.sciencedirect.com/science/article/pii/S1364815216302596 [115] Thiebault T, Fougere L, Destandau E, Réty M, Jacob J. Temporal dynamics of human-excreted pollutants in wastewater treatment plant influents:Toward a better knowledge of mass load fluctuations. Science of the Total Environment, 2017, 596:246-255 http://europepmc.org/abstract/MED/28433767 [116] Huang X Q, Han H G, Qiao J F. Energy consumption model for wastewater treatment process control. Water Science & Technology, 2013, 67(3):667-671 http://www.ncbi.nlm.nih.gov/pubmed/23202574 [117] Han H G, Wu X L, Qiao J F. A self-organizing sliding-mode controller for wastewater treatment processes. IEEE Transactions on Control Systems Technology, 2018, PP(99):1-12 http://ieeexplore.ieee.org/document/8365103/ [118] Zhu S G, Han H G, Guo M, Qiao J F. A data-derived soft-sensor method for monitoring effluent total phosphorus. Chinese Journal of Chemical Engineering, 2017, 25(12):1791-1797 doi: 10.1016/j.cjche.2017.06.008 [119] Han H G, Zhang S, Qiao J F. An adaptive growing and pruning algorithm for designing recurrent neural network. Neurocomputing, 2017, 242(14):51-62 http://www.sciencedirect.com/science/article/pii/S0925231217303296 [120] Han H G, Liu Z, Ge L M, Qiao J F. Prediction of sludge bulking using the knowledge-leverage-based fuzzy neural network. Water Science & Technology, 2018, 77(3):617-627 http://europepmc.org/abstract/MED/29431706 -
下载:
图(3)
计量
- 文章访问数: 2539
- HTML全文浏览量: 473
- PDF下载量: 910
- 被引次数: 0
