1 |
常尧枫, 谢嘉玮, 谢军祥, 等. 城镇污水处理厂提标改造技术研究进展[J]. 中国给水排水, 2022, 38(6): 20⁃28.
|
|
CHANG Y F, XIE J W, XIE J X, et al. Research progress on upgrading and Reconstruction technology of urban sewage treatment plants[J]. China Water & Wastewater, 2022, 38(6): 20⁃28.
|
2 |
GERNAEY K V, VAN LOOSDRECHT M C M, HENZE M, et al. Activated sludge wastewater treatment plant modelling and simulation: State of the art[J]. Environmental Modelling & Software, 2004, 19(9): 763⁃783.
|
3 |
韩红桂, 伍小龙, 张璐, 等. 城市污水处理过程异常工况识别和抑制研究[J]. 自动化学报, 2018, 44(11): 1971⁃1984.
|
|
HAN H G, WU X L, ZHANG L, et al. Identification and suppression of abnormal conditions in municipal wastewater treatment process[J]. Acta Automatica Sinica, 2018, 44(11): 1971⁃1984.
|
4 |
李云, 蒋进元, 何连生, 等. 污水处理故障诊断方法的研究现状及展望[J]. 给水排水, 2017, 43(10): 130⁃135.
|
|
LI Y, JIANG J Y, HE L S, et al. Trouble shooting methods for wastewater treatment: Status and prospect[J]. Water & Wastewater Engineering, 2017, 43(10): 130⁃135.
|
5 |
马小博, 刘鸿斌. 废水处理过程的降维方法综述[J]. 造纸科学与技术, 2022, 41(1): 1⁃11.
|
|
MA X B, LIU H B. Review on dimensionality reduction methods in wastewater treatment processes[J]. Paper Science & Technology, 2022, 41(1): 1⁃11.
|
6 |
高嵩, 邱勇, 孟凡琳, 等. 污水处理工艺数据分析技术的现状与趋势[J]. 环境工程, 2022, 40(6): 194⁃203.
|
|
GAO S, QIU Y, MENG F L, et al. State⁃of⁃art and trends of data analytical techniques for wastewater treatment processes[J]. Environmental Engineering, 2022, 40(6): 194⁃203.
|
7 |
黄道平, 邱禹, 刘乙奇, 等. 面向污水处理的数据驱动故障诊断及预测方法综述[J]. 华南理工大学学报(自然科学版), 2015(3): 111⁃120.
|
|
HUANG D P, QIU Y, LIU Y Q, et al. Review of data⁃driven fault diagnosis and prognosis for wastewater treatment[J]. Journal of South China University of Technology(Natural Science Edition), 2015(3): 111⁃120.
|
8 |
ALVI M, BATSTONE D, MBAMBA C K, et al. Deep learning in wastewater treatment: A critical review[J]. Water Research, 2023, 245: 120518.
|
9 |
李长波, 赵国峥, 徐磊. 水污染控制工程[M]. 北京: 中国石化出版社, 2016.
|
10 |
HOO K A, PIOVOSO M J, SCHNELLE P D, et al. Process and controller performance monitoring: Overview with industrial applications[J]. International Journal of Adaptive Control and Signal Processing, 2003, 17(7⁃9): 635⁃662.
|
11 |
SCHÄFER J, CINAR A. Multivariable MPC system performance assessment, monitoring, and diagnosis[J]. Journal of Process Control, 2004, 14(2): 113⁃129.
|
12 |
LEE C, CHOI S W, LEE I B. Sensor fault diagnosis in a wastewater treatment process[J]. Water Science and Technology, 2006, 53(1): 251⁃257.
|
13 |
GHINEA L M, MIRON M, BARBU M. Semi⁃supervised anomaly detection of dissolved oxygen sensor in wastewater treatment plants[J]. Sensors, 2023, 23(19): 8022.
|
14 |
ISERMANN R. Process fault detection based on modeling and estimation methods[J]. IFAC Proceedings Volumes, 1982, 15(4): 7⁃30.
|
15 |
WEN Q J, GE Z Q, SONG Z H. Data⁃based linear Gaussian state⁃space model for dynamic process monitoring[J]. AIChE Journal, 2012, 58(12): 3763⁃3776.
|
16 |
CHOW E, WILLSKY A. Analytical redundancy and the design of robust failure detection systems[J]. IEEE Transactions on Automatic Control, 1984, 29(7): 603⁃614.
|
17 |
MID E C, DUA V. Fault detection in wastewater treatment systems using multiparametric programming[J]. Processes, 2018, 6(11): 231.
|
18 |
李鸿, 蔺小林, 李建全, 等. 一类污水处理数学模型的定性分析[J]. 陕西科技大学学报, 2024, 42(1): 206⁃212.
|
|
LI H, LIN X L, LI J Q, et al. Qualitative analysis of a mathematical model for sewage treatment[J]. Journal of Shaanxi University of Science & Technology, 2024, 42(1): 206⁃212.
|
19 |
SECO A, RUANO M V, RUIZ⁃MARTINEZ A, et al. Plant⁃wide modelling in wastewater treatment: Showcasing experiences using the biological nutrient removal model[J]. Water Science and Technology, 2020, 81(8): 1700⁃1714.
|
20 |
DOCHAIN D, VANROLLEGHEM P A. Dynamical modelling and estimation in wastewater treatment processes[M]. Caxton: IWA Publishing, 2001.
|
21 |
BARBU M, CARAMAN S, IFRIM G A, et al. State observers for food industry wastewater treatment processes[J]. Journal of Environmental Protection and Ecology, 2011, 12(2): 678⁃687.
|
22 |
YIN X Y, LIU J F. State estimation of wastewater treatment plants based on model approximation[J]. Computers & Chemical Engineering, 2018, 111: 79⁃91.
|
23 |
LI X J, LAW A W, YIN X Y. Partition⁃based distributed extended Kalman filter for large⁃scale nonlinear processes with application to chemical and wastewater treatment processes[J]. AIChE Journal, 2023, 69(12): e18229.
|
24 |
NAGY K A M, MARX B, MOUROT G, et al. State estimation of two⁃time scale multiple models. Application to wastewater treatment plant[J]. Control Engineering Practice, 2011, 19(11): 1354⁃1362.
|
25 |
AGUIAR A P, HADJ⁃ABDELKADER O. Comparison of several filtering approaches on water treatment processes[J]. International Journal Bioautomation, 2021, 25(3): 225⁃248.
|
26 |
宋阳, 姜成英, 王爱杰, 等. 城市污水处理厂活性污泥生物泡沫研究进展[J]. 微生物学通报, 2019, 46(8): 1954⁃1970.
|
|
SONG Y, JIANG C Y, WANG A J, et al. Research progress towards biological foaming of activated sludge in municipal wastewater treatment plants[J]. Microbiology, 2019, 46(8): 1954⁃1970.
|
27 |
DOKAS I M, KARRAS D A, PANAGIOTAKOPOULOS D C. Fault tree analysis and fuzzy expert systems: Early warning and emergency response of landfill operations[J]. Environmental Modelling & Software, 2009, 24(1): 8⁃25.
|
28 |
周志杰, 刘涛源, 胡冠宇, 等. 一种基于数据可靠性和区间证据推理的故障检测方法[J]. 自动化学报, 2020, 46(12): 2628⁃2637.
|
|
ZHOU Z J, LIU T Y, HU G Y, et al. A fault detection method based on data reliability and interval evidence reasoning[J]. Acta Automatica Sinica, 2020, 46(12): 2628⁃2637.
|
29 |
杨帆, 萧德云. 概率SDG模型及故障分析推理方法[J]. 控制与决策, 2006, 21(5): 487⁃491.
|
|
YANG F, XIAO D Y. Probabilistic SDG model and approach to inference for fault analysis[J]. Control and Decision, 2006, 21(5): 487⁃491.
|
30 |
PUÑAL A, ROCA E, LEMA J M. An expert system for monitoring and diagnosis of anaerobic wastewater treatment plants[J]. Water Research, 2002, 36(10): 2656⁃2666.
|
31 |
CALISE F, EICKER U, SCHUMACHER J, et al. Wastewater treatment plant: Modelling and validation of an activated sludge process[J]. Energies, 2020, 13(15): 3925.
|
32 |
CARRASCO E P, RODRÍGUEZ J, PUAL A, et al. Rule⁃based diagnosis and supervision of a pilot⁃scale wastewater treatment plant using fuzzy logic techniques[J]. Expert Systems with Applications, 2002, 22(1): 11⁃20.
|
33 |
PIADEH F, AHMADI M, BEHZADIAN K. Reliability assessment for hybrid systems of advanced treatment units of industrial wastewater reuse using combined event tree and fuzzy fault tree analyses[J]. Journal of Cleaner Production, 2018, 201: 958⁃973.
|
34 |
李子怡, 钟炜. 智慧水厂能耗监测评价与异常诊断管理平台研究[J]. 给水排水, 2024, 50(2): 153⁃157.
|
|
LI Z Y, ZHONG W. Research on the management platform of energy consumption monitoring and abnormal diagnosis in wastewater treatment plants[J]. Water & Wastewater Engineering, 2024, 50(2): 153⁃157.
|
35 |
龚利民, 林峰, 李震, 等. 基于数字孪生的智慧污水处理厂建设和应用实践[J]. 给水排水, 2023, 49(11): 138⁃143.
|
|
GONG L M, LIN F, LI Z, et al. Smart wastewater treatment plant construction and application based on digital twins[J]. Water & Wastewater Engineering, 2023, 49(11): 138⁃143.
|
36 |
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.
|
37 |
YOO C K, VANROLLEGHEM P A, LEE I B. Nonlinear modeling and adaptive monitoring with fuzzy and multivariate statistical methods in biological wastewater treatment plants[J]. Journal of Biotechnology, 2003, 105(1⁃2): 135⁃163.
|
38 |
TAO E P, SHEN W H, LIU T L, et al. Fault diagnosis based on PCA for sensors of laboratorial wastewater treatment process[J]. Chemometrics and Intelligent Laboratory Systems, 2013, 128: 49⁃55.
|
39 |
STAEY G V D, GINS G, SMETS I. Bioflocculation and activated sludge separation: A PLS case study[J]. IFAC⁃PapersOnLine, 2016, 49(7): 1151⁃1156.
|
40 |
AGUADO D, NORIEGA⁃HEVIA G, FERRER J, et al. PLS⁃based soft⁃sensor to predict ammonium concentration evolution in hollow fibre membrane contactors for nitrogen recovery[J]. Journal of Water Process Engineering, 2022, 47: 102735.
|
41 |
SOBCZYK M, PAJDAK⁃STÓS A, FIAŁKOWSKA E, et al. Multivariate analysis of activated sludge community in full⁃scale wastewater treatment plants[J]. Environmental Science and Pollution Research International, 2021, 28(3): 3579⁃3589.
|
42 |
CHENG H C, LIU Y Q, HUANG D P, et al. Rebooting kernel CCA method for nonlinear quality⁃relevant fault detection in process industries[J]. Process Safety and Environmental Protection, 2021, 149: 619⁃630.
|
43 |
LEE J M, YOO C K, LEE I B. New monitoring technique with an ICA algorithm in the wastewater treatment process[J]. Water Science and Technology, 2003, 47(12): 49⁃56.
|
44 |
JOE Q S. Statistical process monitoring: Basics and beyond[J]. Journal of Chemometrics, 2003, 17(8⁃9): 480⁃502.
|
45 |
ZHOU J, HUANG F N, SHEN W H, et al. Sub⁃period division strategies combined with multiway principle component analysis for fault diagnosis on sequence batch reactor of wastewater treatment process in paper mill[J]. Process Safety and Environmental Protection, 2021, 146: 9⁃19.
|
46 |
LIU H B, ZHANG H, ZHANG Y C, et al. Modeling of wastewater treatment processes using dynamic bayesian networks based on fuzzy PLS[J]. IEEE Access, 2020, 8: 92129⁃92140.
|
47 |
梁北辰, 戴景民. 偏最小二乘法在系统故障诊断中的应用[J]. 哈尔滨工业大学学报, 2020, 52(3): 156⁃164.
|
|
LIANG B C, DAI J M. Application of PLS in system fault diagnosis[J]. Journal of Harbin Institute of Technology, 2020, 52(3): 156⁃164.
|
48 |
刘鸿斌, 张昊, 景宜, 等. 基于动态核PCA的复杂废水处理过程在线故障检测[J]. 江苏大学学报(自然科学版), 2021, 42(2): 215⁃220.
|
|
LIU H B, ZHANG H, JING Y, et al. Online fault detection of complex wastewater treatment process using dynamic kernel PCA[J]. Journal of Jiangsu University(Natural Science Edition), 2021, 42(2): 215⁃220.
|
49 |
刘乙奇, 黄志鹏, 于广平, 等. 全生命周期污泥膨胀的智能检测和诊断分析[J]. 华南理工大学学报(自然科学版), 2022, 50(6): 91⁃99.
|
|
LIU Y Q, HUANG Z P, YU G P, et al. Full life⁃cycle intelligent detection and diagnosis analysis for sludge bulking[J]. Journal of South China University of Technology(Natural Science Edition), 2022, 50(6): 91⁃99.
|
50 |
何清, 李宁, 罗文娟, 等. 大数据下的机器学习算法综述[J]. 模式识别与人工智能, 2014, 27(4): 327⁃336.
|
|
HE Q, LI N, LUO W J, et al. A survey of machine learning algorithms for big data[J]. Pattern Recognition and Artificial Intelligence, 2014, 27(4): 327⁃336.
|
51 |
SINGH N K, YADAV M, SINGH V, et al. Artificial intelligence and machine learning⁃based monitoring and design of biological wastewater treatment systems[J]. Bioresource Technology, 2023, 369: 128486.
|
52 |
HE Y L, LI K, LIANG L L, et al. Novel discriminant locality preserving projection integrated with monte carlo sampling for fault diagnosis[J]. IEEE Transactions on Reliability, 2023, 72(1): 166⁃176.
|
53 |
ZHANG N, XU Y, ZHU Q X, et al. Farthest⁃nearest distance neighborhood and locality projections integrated with bootstrap for industrial process fault diagnosis[J]. IEEE Transactions on Industrial Informatics, 2023, 19(5): 6284⁃6294.
|
54 |
WANG Z J, YAN X Y, LI Z W, et al. Nonlinear dynamic process robust monitoring based on spatiotemporal preserving projections with rank⁃order distance[J]. IEEE Transactions on Instrumentation and Measurement, 2023, 72: 1⁃11.
|
55 |
LIU Z J, WAN J Q, MA Y W, et al. Online prediction of effluent COD in the anaerobic wastewater treatment system based on PCA⁃LSSVM algorithm[J]. Environmental Science and Pollution Research International, 2019, 26(13): 12828⁃12841.
|
56 |
马帅印, 王晨, 卢津, 等. 数据驱动的污水处理高密池混凝加药预测研究[J]. 给水排水, 2024, 50(2): 158⁃166.
|
|
MA S Y, WANG C, LU J, et al. Research on data⁃driven prediction of coagulation dosing in high⁃density pools for sewage treatment[J]. Water & Wastewater Engineering, 2024, 50(2): 158⁃166.
|
57 |
LOY⁃BENITEZ J, TARIQ S, NGUYEN H T, et al. Sludge bulking monitoring in industrial wastewater treatment plants through graphical methods: A dynamic graph embedding and Bayesian networks approach[J]. Journal of Environmental Management, 2023, 345: 118804.
|
58 |
KAZEMI P, BENGOA C, STEYER J P, et al. Data⁃driven techniques for fault detection in anaerobic digestion process[J]. Process Safety and Environmental Protection, 2021, 146: 905⁃915.
|
59 |
HINTON G E, OSINDERO S, TEH Y W. A fast learning algorithm for deep belief Nets[J]. Neural Computation, 2006, 18(7): 1527⁃1554.
|
60 |
夏起磊, 罗林, 张垚. 基于注意力增强型编解码网络的化工过程故障诊断[J]. 辽宁石油化工大学学报, 2024, 44(2): 63⁃70.
|
|
XIA Q L, LUO L, ZHANG Y. Fault diagnosis of chemical processes based on attention⁃enhanced encoder⁃decoder network[J]. Journal of Liaoning Petrochemical University, 2024, 44(2): 63⁃70.
|
61 |
谷小峰, 马庆鲁, 黄文杰, 等. 实时参数整定的无模型自适应控制算法及其在气体分馏装置的应用[J]. 石油炼制与化工, 2024, 55(3): 97⁃106.
|
|
GU X F, MA Q L, HUANG W J, et al. Model⁃free adaptive control with real⁃time parameter tuning and its application in gas fractionation unit[J]. Petroleum Processing and Petrochemicals, 2024, 55(3): 97⁃106.
|
62 |
赵学良, 贾梦达, 王显鹏, 等. 石化智能工厂建设关键场景与技术[J]. 化工进展, 2024, 43(2): 894⁃902.
|
|
ZHAO X L, JIA M D, WANG X P, et al. Key scenarios and technologies for smart plant construction in petrochemical industry[J]. Chemical Industry and Engineering Progress, 2024, 43(2): 894⁃902.
|
63 |
常亚娜, 武锦涛, 代玉强. 数据分析与BP神经网络相结合的乙烯装置智能故障诊断系统[J]. 石油炼制与化工, 2023, 54(6): 97⁃104.
|
|
CHANG Y N, WU J T, DAI Y Q. Research on intelligent fault diagnosis system of ethylene plant based on data analysis and bp neural network[J]. Petroleum Processing and Petrochemicals, 2023, 54(6): 97⁃104.
|
64 |
邱磊, 张汉文, 徐辰雨, 等. 基于深度学习的页岩气产量预测模型[J]. 当代化工, 2024, 53(11): 2713⁃2719.
|
|
QIU L, ZHANG H W, XU C Y, et al. Shale gas production prediction model based on deep learning[J]. Contemporary Chemical Industry, 2024, 53(11): 2713⁃2719.
|
65 |
王俊, 邱爽, 鞠丹阳, 等. 基于改进INFO⁃CNN⁃QRGRU模型的农村分布式光伏发电短期概率预测[J]. 沈阳农业大学学报, 2024, 55(4): 490⁃502.
|
|
WANG J, QIU S, JU D Y, et al. Short⁃term probabilistic prediction of rural distributed photovoltaic power generation based on improved INFO⁃CNN⁃QRGRU model[J]. Journal of Shenyang Agricultural University, 2024, 55(4): 490⁃502.
|
66 |
冯勇, 张校铭. 基于MSA⁃LSTM的短期电力负荷预测模型[J]. 辽宁大学学报(自然科学版), 2024, 51(4): 360⁃367.
|
|
FENG Y, ZHANG X M. Short⁃term power load forecasting model based on MSA⁃LSTM[J]. Journal of Liaoning University(Natural Science Edition), 2024, 51(4): 360⁃367.
|
67 |
赵珣, 陈帅, 邱海洋. 基于改进双向循环神经网络的变压器故障诊断模型研究[J]. 辽宁石油化工大学学报, 2023, 43(5): 75⁃83.
|
|
ZHAO X, CHEN S, QIU H Y. Research on transformer fault diagnosis model based on improved bidirectional recurrent neural network[J]. Journal of Liaoning Petrochemical University, 2023, 43(5): 75⁃83.
|
68 |
高坤, 黄雁, 马冰冰, 等. 基于改进优化算法的轧机滚动轴承深度学习故障诊断方法[J]. 辽宁大学学报(自然科学版), 2023, 50(1): 28⁃37.
|
|
GAO K, HUANG Y, MA B B, et al. Deep learning⁃based fault diagnosis method for rolling bearing of rolling mill with improved optimization algorithm[J]. Journal of Liaoning University(Natural Science Edition), 2023, 50(1): 28⁃37.
|
69 |
梁伟阁, 张钢, 王健, 等. 复杂机械设备健康状态预测方法研究综述[J]. 兵器装备工程学报, 2022, 43(7): 67⁃77.
|
|
LIANG W G, ZHANG G, WANG J, et al. A review on health state assessment and remaining useful life prediction of mechanical equipment under intelligent manufacturing[J]. Journal of Ordnance Equipment Engineering, 2022, 43(7): 67⁃77.
|
70 |
黄梓健, 石磊, 杨晓, 等. 基于多智能体的罐区巡检关键技术研究[J]. 当代化工, 2022, 51(1): 236⁃241.
|
|
HUANG Z J, SHI L, YANG X, et al. Research on key technologies of tank farm inspection based on multi⁃agent[J]. Contemporary Chemical Industry, 2022, 51(1): 236⁃241.
|
71 |
MUNIAPPAN A, TIRTH V, ALMUJIBAH H, et al. Deep convolutional neural network with sine cosine algorithm based wastewater treatment systems[J]. Environmental Research, 2023, 219: 114910.
|
72 |
HU T, ZHANG Y C, WANG X Y, et al. Optimized convolutional neural networks for fault diagnosis in wastewater treatment processes[J]. Environmental Science: Water Research & Technolog, 2024, 10(2): 364⁃375.
|
73 |
WANG G M, YUAN G H, HU Z Q, et al. Complexity⁃based structural optimization of deep belief network and application in wastewater treatment process[J]. IEEE Transactions on Industrial Informatics, 2024, 20(4): 6974⁃6982.
|
74 |
陈国健, 李继庚, 陈波, 等. 基于自编码的长流程造纸过程断纸故障识别[J]. 中国造纸, 2024, 43(3): 113⁃120.
|
|
CHEN G J, LI J G, CHEN B, et al. Paper break fault recognition in long process papermaking process based on autoencoder[J]. China Pulp & Paper, 2024, 43(3): 113⁃120.
|
75 |
SESHAN S, VRIES D, IMMINK J, et al. LSTM⁃based autoencoder models for real⁃time quality control of wastewater treatment sensor data[J]. Journal of Hydroinformatics, 2024, 26(2): 441⁃458.
|