Citation: | XU Runze, CAO Jiashun, FANG Fang. RESEARCH PROGRESS ON N2O RECYCLING AND DATA-DRIVEN MODELING IN WASTEWATER TREATMENT PROCESSES[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(6): 107-115. doi: 10.13205/j.hjgc.202206014 |
[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.
|