[1] 张道广.基于乡村振兴背景下农村污水治理应用现状及发展研究[J].清洗世界,2023,39(09):120-122.[2] Ching P M L, SO R H Y, MORCK T. Advances in soft sensors for wastewater treatment plants: A systematic review[J]. Journal of Water Process Engineering,2021,44: 102367.
[3] Zhu S, HAN H, GUO M, et al. A data-derived soft-sensor method for monitoring effluent total phosphorus[J]. Chinese Journal of Chemical Engineering,2017,25(12): 1791-1797.
[4] Sapmaz A, Kurban S, Dundar A G, et al. Deep Learning Based Soft Sensor to Predict Total Suspended Solids of Refinery Water Treatment Plant Using Real Process Data[J]. IFAC-PapersOnLine,2022,55(33): 60-65.
[5] Kovacs D J, Li Z, Baetz B W, et al. Membrane fouling prediction and uncertainty analysis using machine learning: A wastewater treatment plant case study[J]. Journal of Membrane Science,2022,660:120817.
[6] Ba-Alawi A H, Al-masni M A, Yoo C. Simultaneous sensor fault diagnosis and reconstruction for intelligent monitoring in wastewater treatment plants: An explainable deep multi-task learning model[J]. Journal of Water Process Engineering,2023,55:104119.
[7] Zhu J, Jiang Z, Feng L. Improved neural network with least square support vector machine for wastewater treatment process[J]. Chemosphere,2022,308:136116.
[8] Wu X, Zheng Z, Wang L, et al. Coupling process-based modeling with machine learning for long-term simulation of wastewater treatment plant operations[J]. Journal of Environmental Management, 2023,341:118116.
[9] Zhang Y, Li C, Duan H, et al. Deep learning based data-driven model for detecting time-delay water quality indicators of wastewater treatment plant influent[J]. Chemical Engineering Journal,2023,467:143483.
[10] Farhi N, Kohen E, Mamane H, et al. Prediction of wastewater treatment quality using LSTM neural network[J]. Environmental Technology & Innovation, 2021,23:101632.
[11] Zaghloul M S, Achari G. Application of machine learning techniques to model a full-scale wastewater treatment plant with biological nutrient removal[J]. Journal of Environmental Chemical Engineering,2022, 10(3):107430.
[12] Jia R, Song Y, Piao D, et al. Exploration of deep learning models for real-time monitoring of state and performance of anaerobic digestion with online sensors[J]. Bioresource Technology,2022,363:127908.
[13] Jang J, Abbas A, Kim M, et al. Prediction of antibiotic-resistance genes occurrence at a recreational beach with deep learning models[J]. Water Research,2021, 196:117001.
[14] 姚亭吉,刘娴,张爱茜.基于机器学习的环境污染物EGFR结合活性的预测研究:中国毒理学会第十次全国毒理学大会[C],中国广东珠海,2023.
[15] Li G, Ji J, Ni J, et al. Application of deep learning for predicting the treatment performance of real municipal wastewater based on one-year operation of two anaerobic membrane bioreactors[J]. Science of The Total Environment,2022,813:151920.
[16] 张成彬,邵星,徐燕萍,等.基于物联网与深度学习的污水处理智能监控系统设计[J].软件导刊,2017,16(10):89-91.
[17] Peng C, Zeyu L, Gongming W, et al. An effective deep recurrent network with high-order statistic information for fault monitoring in wastewater treatment process[J]. Expert Systems with Applications,2021,167:114141.
[18] 齐晗兵,李怀治,王秋实,等.光谱预处理-卷积神经网络模型预测油田污水含油量研究[J].当代化工,2023,52(02): 407-410.
[19] 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.
[20] Chen K, Wang H, Valverde-Pérez B, et al. Optimal control towards sustainable wastewater treatment plants based on multi-agent reinforcement learning[J]. Chemosphere, 2021,279:130498.
[21] Xu R, Cao J, 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.
[22] 鲁树武,伍小龙,郑江,等.基于动态融合LOF的城市污水处理过程数据清洗方法[J].控制与决策,2022,37(05): 1231-1240.
[23] 姜奕圻.基于多源数据融合的城市排水管网水质预测模型研究[D].哈尔滨.哈尔滨工业大学,2022.
[24] 郭利进,李博仑.基于GNFA-SVR污水出水总氮软测量研究[J].工业水处理,2022,42(10):111-117.
[25] 钱维坤,毕新忠,张蕊,等.数据驱动的油田污水处理水质预测研究[J].工业安全与环保,2023,49(04):77-81.
[26] 陈园,李明河,朱琳.基于BAS优化SVM的出水COD软测量建模[J].广东化工,2020,47(21):107-109.
[27] Wang G, Jia Q, Zhou M, et al. Soft-sensing of Wastewater Treatment Process via Deep Belief Network with Event-triggered Learning[J]. Neurocomputing,2021, 436:103-113.
[28] 王旭,钟炜.一种污水处理厂设备预测及辅助维护智能平台[J].中国给水排水,2023,39(10):121-125.
[29] 赵超,戴坤成,王贵评,等.基于AWLS-SVM的污水处理过程软测量建模[J].仪器仪表学报,2015,36(08):1792-1800.
[30] 黄学平,吴留兴,辛攀,等.基于3种机器学习模型的污水处理厂出水总氮预测分析[J].能源研究与管理, 2023(02): 100-105.
[31] 赵帅,李妍君,熊伟丽.基于KPCA-Bagging的高斯过程回归建模方法及应用[J].控制工程,2019,26(01): 131-136.
[32] Wang K, Zhang H, Shen Y, et al. Impact of salinity on anaerobic ceramic membrane bioreactor for textile wastewater treatment: Process performance, membrane fouling and machine learning models[J]. Journal of Environmental Management,2023,345:118717.
[33] Nguyen X C, Ly Q V, Peng W, et al. Vertical flow constructed wetlands using expanded clay and biochar for wastewater remediation: A comparative study and prediction of effluents using machine learning[J]. Journal of Hazardous Materials,2021,413:125426.
[34] Wang D, Thunéll S, Lindberg U, et al. A machine learning framework to improve effluent quality control in wastewater treatment plants[J].Science of The Total Environment,2021,784:147138.
[35] Qambar A S, Al Khalidy M M. Optimizing dissolved oxygen requirement and energy consumption in wastewater treatment plant aeration tanks using machine learning[J]. Journal of Water Process Engineering, 2022,50: 103237.
[36] Yang B, Xiao Z, Meng Q, et al. Deep learning-based prediction of effluent quality of a constructed wetland[J]. Environmental Science and Ecotechnology,2023,13: 100207.
[37] Jeong K, Abbas A, Shin J, et al. Prediction of biogas production in anaerobic co-digestion of organic wastes using deep learning models[J]. Water Research,2021,205: 117697.
[38] 周长亮,苗盛,王明丽,等.基于深度学习和物联网技术的智慧污水管控系统[J].安全与环境工程,2021,28(01): 191-196.
[39] Chang P, Li Z. Over-complete deep recurrent neutral network based on wastewater treatment process soft sensor application[J]. Applied Soft Computing,2021,105: 107227.
[40] 薛同来,赵冬晖,韩菲,等.SVR在城市污水BOD预测中的应用[J].新型工业化,2019,9(04):94-98.
[41] 周铭睿,曲江北,李彭,等.分散式农村污水基于三维荧光光谱和紫外-可见全波段吸收光谱的“聚类-回归”COD预测模型[J].光谱学与光谱分析,2022,42(07):2113-2119.
[42] Niu C, Tan K, Jia X, et al. Deep learning based regression for optically inactive inland water quality parameter estimation using airborne hyperspectral imagery[J]. Environmental Pollution,2021,286:117534.
[43] Wang X, Kvaal K, Ratnaweera H. Explicit and interpretable nonlinear soft sensor models for influent surveillance at a full-scale wastewater treatment plant[J]. Journal of Process Control,2019,77:1-6.
[44] Wijaya J, Oh S. Machine learning reveals the complex ecological interplay of microbiome in a full-scale membrane bioreactor wastewater treatment plant[J]. Environmental Research,2023,222:115366.
[45] 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.
[46] 韩红桂,张璐,乔俊飞.一种基于自适应回归核函数的污水处理能耗模型[J].化工学报,2016,67(03):947-953.
[47] Szel?g B, Zaborowska E, M?kinia J. An algorithm for selecting a machine learning method for predicting nitrous oxide emissions in municipal wastewater treatment plants[J]. Journal of Water Process Engineering,2023,54:103939.
[48] 李畅,潘丰.基于XGBoost的出水氨氮和总磷浓度的软测量研究[J].计算机与数字工程,2021,49(05):1003-1008.
[49] Bellamoli F, Di Iorio M, Vian M, et al. Machine learning methods for anomaly classification in wastewater treatment plants[J]. Journal of Environmental Management,2023,344:118594.
[50] Liu H, Zhang Y, Zhang H. Prediction of effluent quality in papermaking wastewater treatment processes using dynamic kernel-based extreme learning machine[J]. Process Biochemistry,2020,97:72-79.
[51] Zhao F, Liu M, Wang K, et al. A soft measurement approach of wastewater treatment process by lion swarm optimizer-based extreme learning machine[J]. Measurement, 2021,179:109322.
[52] Safder U, Loy-Benitez J, Nguyen H, et al. A hybrid extreme learning machine and deep belief network framework for sludge bulking monitoring in a dynamic wastewater treatment process[J]. Journal of Water Process Engineering, 2022,46:102580.
[53] 于丽昕,张家昌,韩红桂,等.城市污水处理水质多尺度自适应特征提取[J].控制工程,2023,30(03):552-559.
[54] Wang R, Yu Y, Chen Y, et al. Model construction and application for effluent prediction in wastewater treatment plant: Data processing method optimization and process parameters integration[J]. Journal of Environmental Management,2022,302:114020.
[55] Shen Y, Li H, Zhang B, et al. An artificial neural network-based data filling approach for smart operation of digital wastewater treatment plants[J]. Environmental Research,2023,224:115549.
[56] Chang P, Li Z. Over-complete deep recurrent neutral network based on wastewater treatment process soft sensor application[J]. Applied Soft Computing,2021,105: 107227.
[57] Ching P M L, Zou X, Wu D, et al. Development of a wide-range soft sensor for predicting wastewater BOD5 using an eXtreme gradient boosting (XGBoost) machine[J]. Environmental Research,2022,210:112953.
[58] 裴力锋,陈伟杰,徐敬生,等.基于自注意力机制的污水处理厂精确加药模型预测控制[J].环境工程,2023:1-14.
[59] 刘杰,李佟,李军.基于改进支持向量回归机的污水处理厂出水总氮预测模型[J].环境工程学报,2018,12(01): 119-126.
[60] Zhang H, Yang C, Shi X, et al. Effluent quality prediction in papermaking wastewater treatment processes using dynamic Bayesian networks[J]. Journal of Cleaner Production,2021,282:125396.
[61] Xiong J, Tao Z, Hua L, et al. An evolutionary robust soft measurement technique via enhanced atom search optimization and outlier robust extreme learning machine for wastewater treatment process[J]. Journal of Water Process Engineering,2023,55:104102.[62] 柴伟,池彬彬.基于区间预测模型的污水处理厂传感器故障检测[J].计算技术与自动化,2020,39(01):23-28.
[63] 邱勇,刘雪洁,田宇心,等.一种水质监测物联网传感器的标定装置及方法:CN202210713258.0[P].2023-08-08.