污水处理系统中N<sub>2</sub>O的生产利用及数据驱动模拟研究进展

徐润泽; 操家顺; 方芳

doi:10.13205/j.hjgc.202206014

污水处理系统中N₂O的生产利用及数据驱动模拟研究进展

doi: 10.13205/j.hjgc.202206014

河海大学环境学院浅水湖泊综合治理与资源开发教育部重点实验室, 南京 210024

基金项目:

国家自然科学基金面上项目"胞内存储物介导下硫自养/异养协同反硝化系统中不同微生物间相互作用机制研究"(52170032)

国家自然科学基金面上项目"温室气体N₂O为电子受体条件下活性污泥胞内PHA的合成机制研究"(51878244)

详细信息

作者简介:
徐润泽(1995-),男,在读博士研究生,主要研究方向为污水资源化。runzexu@hhu.edu.cn

通讯作者:
方芳(1982-),女,副教授,主要研究方向为污水处理及资源化。ffang65@hhu.edu.cn

计量
- 文章访问数: 247
- HTML全文浏览量: 42
- PDF下载量: 9
- 被引次数: 0
出版历程
- 收稿日期: 2022-01-01
- 网络出版日期: 2022-09-01
- 刊出日期: 2022-09-01

RESEARCH PROGRESS ON N₂O RECYCLING AND DATA-DRIVEN MODELING IN WASTEWATER TREATMENT PROCESSES

Key Laboratory of Integrates Regulation and Resource Development on Shallow Lakes, Ministry of Education, College of Environment, Hohai University, Nanjing 210024, China

摘要

摘要: 氧化亚氮(N₂O)是一种温室气体,同时也是具有能源回收潜力的强氧化性物质。综述了促进N₂O产生的新兴污水脱氮过程及提高N₂O产生的方法,比较了不同方法的运行条件及N₂O转化率,并指出了各种方法的不足之处。从识别N₂O产生的关键影响因素和预测N₂O产量2个方面综述了污水处理过程中N₂O数据驱动模型的研究进展。目前N₂O的增产方法主要包括耦合好氧-缺氧氮分解过程、单反应器生产过程及基因工程菌和半导体修饰菌增产过程。收集污水处理厂中的大数据可以建立N₂O数据驱动模型,但是现有的数据驱动模型仅仅关注N₂O减排。开发N₂O的新型增产过程,优化控制增产过程的功能菌种,建立N₂O数据驱动模型与N₂O增产方法之间的关联性是未来N₂O生产利用技术的发展方向。
- 氧化亚氮 /
- 污水处理 /
- 机器学习 /
- 能源回收 /
- 脱氮
Abstract: Nitrous oxide (N₂O) is a greenhouse gas and a strong oxidizing substance with the potential of energy recovery.This paper critically reviewed the novel nitrogen removal bioprocesses and methods for increasing N₂O production from wastewater.The operating conditions and N₂O conversion efficiencies in these bioprocesses were compared.Then,the shortages of these methods were pointed out.Moreover,this paper comprehensively reviewed the research progress on data-driven modeling of N₂O emission in wastewater treatment processes from two perspectives:1) identifying key factors related to N₂O,and 2) predicting N₂O production.Currently,the main methods for recovering N₂O include coupled aerobic-anoxic nitrous decomposition operation (CANDO),single reactor process,applications of recombinant strains or semiconductor modification strains.Big data of wastewater treatment plants could be utilized to establish the data-driven models for N₂O emissions,whereas the existing N₂O models mainly focus on reducing N₂O emission.The future trends of N₂O recovery include:1) developing new methods for recovering N₂O;2) optimizing functional microbes in N₂O recovering processes;3) establishing the relationships between data-driven modeling of N₂O and N₂O recovering processes.
- nitrous oxide /
- wastewater treatment /
- machine learning /
- energy recovery /
- nitrogen removal

HTML全文

参考文献(55)

[1]	巩有奎,任丽芳,彭永臻.不同盐度生活污水硝化及N₂O释放特性[J].水处理技术, 2019, 45(9):99-103.
[2]	何德明,尹志轩,刘长青,等.生物脱氮工艺过程中N₂O的释放机理及减排影响因素研究进展[J].环境污染与防治, 2021, 43(8):1054-1061.
[3]	巩有奎,任丽芳,罗佩云,等. NaCl浓度对SBBR同步脱氮及N₂O释放的影响[J].农业工程学报, 2020, 36(3):152-159.
[4]	巩有奎,彭永臻.温度变化对短程生物脱氮及N₂O释放影响[J].水处理技术, 2020, 46(8):110-115.
[5]	裘湛,赵刚,黄翔峰.污水处理厂N₂O的释放特征和减排途径研究[J].环境科学与管理, 2016, 41(4):74-77.
[6]	WU L, PENG L, WEI W, et al. Nitrous oxide production from wastewater treatment:the potential as energy resource rather than potent greenhouse gas[J]. Journal of Hazardous Materials, 2020,387:121694.
[7]	SCHERSON Y D, WELLS G F, WOO S G, et al. Nitrogen removal with energy recovery through N₂O decomposition[J]. Energy&Environmental Science, 2013, 6(1):241-248.
[8]	SCHERSON Y D, WOO S G, CRIDDLE C S. Production of nitrous oxide from anaerobic digester centrate and its use as a co-oxidant of biogas to enhance energy recovery[J]. Environmental Science&Technology, 2014, 48(10):5612-5619.
[9]	ZHAO J Q, HUANG N, HU B, et al. Potential of nitrous oxide recovery from an aerobic/oxic/anoxic SBR process[J]. Water Science and Technology, 2016, 73(5):1061-1066.
[10]	GAO H, LIU M, GRIFFIN J S, et al. Complete nutrient removal coupled to nitrous oxide production as a bioenergy source by denitrifying polyphosphate-accumulating organisms[J]. Environmental Science&Technology, 2017, 51(8):4531-4540.
[11]	NI B J, PENG L, LAW Y, et al. Modeling of nitrous oxide production by autotrophic ammonia-oxidizing bacteria with multiple production pathways[J]. Environmental Science&Technology, 2014, 48(7):3916-3924.
[12]	NI B J, PAN Y, VAN DEN AKKER B, et al. Full-scale modeling explaining large spatial variations of nitrous oxide fluxes in a step-feed plug-flow wastewater treatment reactor[J]. Environmental Science&Technology, 2015, 49(15):9176-9184.
[13]	NI B J, YUAN Z. Recent advances in mathematical modeling of nitrous oxides emissions from wastewater treatment processes[J]. Water Research, 2015, 87:336-346.
[14]	李真.基于ASM2D模型的废水同步脱氮除磷过程的动力学模拟研究[D].广州:华南理工大学, 2020.
[15]	刘玉田,张守彬,邱立平,等.污水生物脱氮过程硝化阶段N₂O动力学模型[J].环境工程学报, 2017, 11(8):4601-4608.
[16]	PEREIRA T D S, SPINDOLA R H, RABELO C, et al. A predictive model for N₂O production in anammox-granular sludge reactors:Combined effects of nitrate/ammonium ratio and organic matter concentration[J]. Journal of Environmental Management, 2021, 297:113295.
[17]	SCHMIDHUBER J. Deep learning in neural networks:an overview[J]. Neural Networks, 2015, 61:85-117.
[18]	XU R Z, CAO J S, LUO J Y, et al. Integrating mechanistic and deep learning models for accurately predicting the enrichment of polyhydroxyalkanoates accumulating bacteria in mixed microbial cultures[J]. Bioresource Technology, 2022,344:126276.
[19]	XU R Z, CAO J S, FENG G, et al. Fast identification of fluorescent components in three-dimensional excitation-emission matrix fluorescence spectra via deep learning[J]. Chemical Engineering Journal, 2021:132893.
[20]	XU R Z, CAO J S, FANG F, et al. Integrated data-driven strategy to optimize the processes configuration for full-scale wastewater treatment plant predesign[J]. Science of the Total Environment, 2021, 785:147356.
[21]	XU R Z, CAO J S, WU Y, et al. An integrated approach based on virtual data augmentation and deep neural networks modeling for VFA production prediction in anaerobic fermentation process[J]. Water Research, 2020, 184:116103.
[22]	NIE H B, DANG Y, YAN H K, et al. Enhanced recovery of nitrous oxide from incineration leachate in a microbial electrolysis cell inoculated with a nosZ-deficient strain of Pseudomonas aeruginosa[J]. Bioresource Technology, 2021, 333:125082.
[23]	NIE H B, LIU X Y, DANG Y, et al. Efficient nitrous oxide recovery from incineration leachate by a nosZ-deficient strain of Pseudomonas aeruginosa[J]. Bioresource Technology, 2020, 297:122371.
[24]	YU K H, CAN F, ERGENEKON P. Nitric oxide and nitrite removal by partial denitrifying hollow-fiber membrane biofilm reactor coupled with nitrous oxide generation as energy recovery[J]. Environmental Technology, 2021:1-14.
[25]	YE J Y, GAO H, DOMINGO-FÉLEZ C, et al. Insights into chronic zinc oxide nanoparticle stress responses of biological nitrogen removal system with nitrous oxide emission and its recovery potential[J]. Bioresource Technology, 2021, 327:124797.
[26]	ZHANG M, GU J, LIU Y. Engineering feasibility, economic viability and environmental sustainability of energy recovery from nitrous oxide in biological wastewater treatment plant[J]. Bioresource Technology, 2019, 282:514-519.
[27]	WANG L K, CHEN X, WEI W, et al. Biological reduction of nitric oxide for efficient recovery of nitrous oxide as an energy source[J]. Environmental Science&Technology, 2021, 55(3):1992-2005.
[28]	YU C, QIAO S, YANG Y, et al. Energy recovery in the form of N₂O by denitrifying bacteria[J]. Chemical Engineering Journal, 2019, 371:500-506.
[29]	CHEN M, ZHOU X F, YU Y Q, et al. Light-driven nitrous oxide production via autotrophic denitrification by self-photosensitized Thiobacillus denitrificans[J]. Environmental Internation, 2019, 127:353-360.
[30]	FANG F, XU R Z, HUANG Y Q, et al. Exploring the feasibility of nitrous oxide reduction and polyhydroxyalkanoates production simultaneously by mixed microbial cultures[J]. Bioresource Technology, 2021,342:126012.
[31]	WANG Z Y, WOO S G, YAO Y N, et al. Nitrogen removal as nitrous oxide for energy recovery:increased process stability and high nitrous yields at short hydraulic residence times[J]. Water Research, 2020, 173:115575.
[32]	WEIßBACH M, THIEL P, DREWES J E, et al. Nitrogen removal and intentional nitrous oxide production from reject water in a coupled nitritation/nitrous denitritation system under real feed-stream conditions[J]. Bioresource Technology, 2018, 255:58-66.
[33]	WEIßBACH M, DREWES J E, KOCH K. Application of the oxidation reduction potential (ORP) for process control and monitoring nitrite in a Coupled Aerobic-anoxic Nitrous Decomposition Operation (CANDO)[J]. Chemical Engineering Journal, 2018, 343:484-491.
[34]	WEIßBACH M, GOSSLER F, DREWES J E, et al. Separation of nitrous oxide from aqueous solutions applying a micro porous hollow fiber membrane contactor for energy recovery[J]. Separation and Purification Technology, 2018, 195:271-280.
[35]	FANG F, XU R Z, HUANG Y Q, et al. Production of polyhydroxyalkanoates and enrichment of associated microbes in bioreactors fed with rice winery wastewater at various organic loading rates[J]. Bioresource Technology, 2019, 292:121978.
[36]	ZHAO Y, ZENG D, WU G. Efficient nitrous oxide production and metagenomics-based analysis of microbial communities in denitrifying systems acclimated with different electron acceptors[J]. International Biodeterioration&Biodegradation, 2019, 138:92-98.
[37]	ZHUGE Y Y, SHEN X Y, LIU Y D, et al. Application of acidic conditions and inert-gas sparging to achieve high-efficiency nitrous oxide recovery during nitrite denitrification[J]. Water Research, 2020, 182:116001.
[38]	WU L, WANG L K, WEI W, et al. Sulfur-driven autotrophic denitrification of nitric oxide for efficient nitrous oxide recovery[J]. Biotechnology and Bioengineering, 2021, 119(1):257-267.
[39]	LIN Z, SUN D, DANG Y, et al. Significant enhancement of nitrous oxide energy yields from wastewater achieved by bioaugmentation with a recombinant strain of Pseudomonas aeruginosa[J]. Scientific Reports, 2018, 8(1):11916.
[40]	LECUN Y, BENGIO Y, HINTON G. Deep learning[J]. Nature, 2015, 521(7553):436-444.
[41]	ZHONG S, ZHANG K, BAGHERI M, et al. Machine learning:new ideas and tools in environmental science and engineering[J]. Environmental Science&Technology, 2021, 55(19):12741-12754.
[42]	ZAGHLOUL M S, IORHEMEN O T, HAMZA R A, et al. Development of an ensemble of machine learning algorithms to model aerobic granular sludge reactors[J]. Water Research, 2021, 189:116657.
[43]	NEWHART K B, HOLLOWAY R W, HERING A S, et al. Data-driven performance analyses of wastewater treatment plants:a review[J]. Water Research, 2019, 157:498-513.
[44]	VASILAKI V, MASSARA T M, STANCHEV P, et al. A decade of nitrous oxide (N₂O) monitoring in full-scale wastewater treatment processes:a critical review[J]. Water Research, 2019, 161:392-412.
[45]	HWANGBO S, AL R, CHEN X, et al. Integrated model for understanding N₂O emissions from wastewater treatment plants:a deep learning approach[J]. Environmental Science&Technology, 2021, 55(3):2143-2151.
[46]	van DIJK E J H, van LOOSDRECHT M C M, PRONK M. Nitrous oxide emission from full-scale municipal aerobic granular sludge[J]. Water Research, 2021, 198:117159.
[47]	SONG M J, CHOI S, BAE W B, et al. Identification of primary effecters of N₂O emissions from full-scale biological nitrogen removal systems using random forest approach[J]. Water Research, 2020,184:116144.
[48]	BAE W B, PARK Y, CHANDRAN K, et al. Temporal triggers of N₂O emissions during cyclical and seasonal variations of a full-scale sequencing batch reactor treating municipal wastewater[J]. Science of the Total Environment, 2021, 797:149093.
[49]	SALTELLI A, ANNONI P, AZZINI I, et al. Variance based sensitivity analysis of model output. design and estimator for the total sensitivity index[J]. Computer Physics Communications, 2010, 181(2):259-270.
[50]	KUCHERENKO S, TARANTOLA S, ANNONI P. Estimation of global sensitivity indices for models with dependent variables[J]. Computer Physics Communications, 2012, 183(4):937-946.
[51]	VASILAKI V, CONCA V, FRISON N, et al. A knowledge discovery framework to predict the N₂O emissions in the wastewater sector[J]. Water Research, 2020, 178:115799.
[52]	DIETRICH R, OPPER M, SOMPOLINSKY H. Statistical mechanics of support vector networks[J]. Physical Review Letters, 1999, 82(14):2975-2978.
[53]	ASADI M, MCPHEDRAN K N. Greenhouse gas emission estimation from municipal wastewater using a hybrid approach of generative adversarial network and data-driven modelling[J]. Science of the Total Environment, 2021, 800:149508.
[54]	VASILAKI V, DANISHVAR S, MOUSAVI A, et al. Data-driven versus conventional N₂O EF quantification methods in wastewater; how can we quantify reliable annual EFs?[J]. Computers&Chemical Engineering, 2020, 141:106997.
[55]	STENTOFT P A, MUNK-NIELSEN T, MOLLER J K, et al. Prioritize effluent quality, operational costs or global warming?-Using predictive control of wastewater aeration for flexible management of objectives in WRRFs[J]. Water Research, 2021, 196:116960.