Source Jouranl of CSCD
Source Journal of Chinese Scientific and Technical Papers
Included as T2 Level in the High-Quality Science and Technology Journals in the Field of Environmental Science
Core Journal of RCCSE
Included in the CAS Content Collection
Included in the JST China
Indexed in World Journal Clout Index (WJCI) Report
MA Xiao-qian, ZHANG Zhe, LIU Chao, WANG Jun-jie, WANG Jia-lin, YU Yi, CAO Rui-jie, SHI Zhi-li, WANG Ya-yi. TREATMENT OF LEACHATE FROM MUNICIPAL SOLID WASTE INCINERATION PLANT BY COMBINED ANAMMOX PROCESS: NITROGEN REMOVAL AND MICROBIAL MECHANISM[J]. ENVIRONMENTAL ENGINEERING , 2021, 39(11): 110-118. doi: 10.13205/j.hjgc.202111014
Citation: PEI Lifeng, CHEN Weijie, XU Jingsheng, LÜ Lu. MODEL PREDICTIVE CONTROL FOR ACCURATE DOSING IN WASTEWATER TREATMENT PLANTS BASED ON SELF-ATTENTION MECHANISM[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(11): 84-92,140. doi: 10.13205/j.hjgc.202311015

MODEL PREDICTIVE CONTROL FOR ACCURATE DOSING IN WASTEWATER TREATMENT PLANTS BASED ON SELF-ATTENTION MECHANISM

doi: 10.13205/j.hjgc.202311015
  • Received Date: 2022-12-08
    Available Online: 2023-12-25
  • Precise control is an effective way to solve the problems of high operating costs and substandard effluent quality of wastewater treatment plants, but due to the non-linear, time-varying, and time-lagging nature of the sewage treatment process, conventional control methods can hardly meet the demand. This study proposed a model predictive control method to achieve accurate dosing of chemicals in sewage treatment plants. The constructed model was based on the self-attention mechanism to extract valid information from the input sequence and to model the complex non-linear relationships between features, thus improving the prediction accuracy. Data from a denitrification filter at a wastewater treatment plant in Jiangsu Province were used for training and testing. Compared with CNN and LSTM, the results showed that the MER and MSE metrics of the self-attention model is the best for the prediction of effluent TN, COD and NH4+-N, achieving more accurate prediction. The predictive control model using a particle swarm optimization algorithm to calculate the dosage was applied to this wastewater treatment plant. The results showed that the effluent achieved more than 95% compliance for TN and the optimized dosage was 28.72% and 21.78% lower than the monthly average dosage of the sewage treatment plant respectively.
  • [1]
    邱勇, 李冰, 刘垚, 等.污水处理厂化学除磷自动控制系统优化研究[J].给水排水, 2016, 52(7):126-129.
    [2]
    侯莹.城市污水处理过程多目标优化控制方法及应用研究[D].北京:北京工业大学, 2018.
    [3]
    杜胜利, 张庆达, 曹博琦, 等.城市污水处理过程模型预测控制研究综述[J].信息与控制, 2022, 51(1):41-53.
    [4]
    孙培德, 杨朋飞, 楼菊青, 等.全耦合活性污泥模型(FCASM3)在A+A2/O工艺污水处理厂中的数值模拟应用[J].环境科学学报, 2018, 38(9):3561-3572.
    [5]
    孙月娣.Bardenpho工艺内回流与碳源投加耦合控制动态模拟[J].中国给水排水, 2017, 33(23):66-70.
    [6]
    吴宇行, 王晓东, 陈宁, 等.典型城镇污水处理厂碳源智能投加控制生产性试验[J].环境工程, 2022, 40(6):212-218

    , 271.
    [7]
    WANG D, THUNELL 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.
    [8]
    LI M, HU S, XIA J, et al.Dissolved oxygen model predictive control for activated sludge process model based on the fuzzy C-means cluster algorithm[J].International Journal of Control, Automation and Systems, 2020, 18:2435-2444.
    [9]
    SUN J, PENG L, YAN X, et al.Reducing aeration energy consumption in a large-scale membrane bioreactor:process simulation and engineering application[J].Water Research, 2016, 93:205-213.
    [10]
    徐承志, 操家顺, 罗景阳, 等.活性污泥数学模型在污水处理中的研究进展[J].应用化工, 2021, 50(5):66-70.
    [11]
    WANG D, THUNELL S, LINDBERG U, et al.Towards better process management in wastewater treatment plants:process analytics based on SHAP values for tree-based machine learning methods[J].Journal of Environmental Management, 2022, 301:113941.
    [12]
    SONG M, CHOI S, BAE W, 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.
    [13]
    YAQUB M, ASIF H, KIM S, et al.Modeling of a full-scale sewage treatment plant to predict the nutrient removal efficiency using a long short-term memory (LSTM) neural network[J].Journal of Water Process Engineering, 2020, 37:101388.
    [14]
    NIU G, YI X, CHEN C, et al.A novel effluent quality predicting model based on genetic-deep belief network algorithm for cleaner production in a full-scale paper-making wastewater treatment[J].Journal of Cleaner Production, 2020, 265:121787.
    [15]
    LECUN Y, BENGIO Y, HINTON G.Deep learning[J].Nature, 2015, 521(7553):436-444.
    [16]
    朱张莉, 饶元, 吴渊, 等.注意力机制在深度学习中的研究进展[J].中文信息学报, 2019, 33(6):1-11.
    [17]
    CHAUDHARI S, MITHAL V, POLATKAN G, et al.An attentive survey of attention models[J].ACM Transactions on Intelligent Systems and Technology, 2021, 12(5):1-32.
    [18]
    FU J, LIU J, TIAN H, et al.Dual Attention Network for Scene Segmentation[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019:3141-3149.
    [19]
    VASWANI A, SHAZEER N, PARMAR N, et al.Attention Is All You Need[C]//Advances in Neural Information Processing System, 2017.
    [20]
    XU J, SUN X, ZHANG Z, et al.Understanding and Improving Layer Normalization[C]//Advances in Neural Information Processing System, 2019:4381-4391.
    [21]
    KENNEDY J, EBERHART R.Particle swarm optimization[C]//1995 IEEE International Conference on Neural Networks Proceedings, 1995:1942-1948.
  • Relative Articles

    [1]WU Yulun, LI Zemin, CHENG Xiaoqian, QIU Guanglei, WEI Chaohai. PREDICTION OF NITROGEN REMOVAL PERFORMANCE AND IDENTIFICATION OF KEY PARAMETERS OF PARTIAL NITRIFICATION/PARTIAL DENITRIFICATION-ANAMMOX PROCESS BASED ON MACHINE LEARNING[J]. ENVIRONMENTAL ENGINEERING , 2024, 42(9): 180-190. doi: 10.13205/j.hjgc.202409017
    [2]ZHANG Zheng, QIU Dahe, JING Zibo, XUE Bo, HU Xinyu. RETINANET-BASED DIRECTED TARGET DETECTION FOR RECYCLABLE WASTE[J]. ENVIRONMENTAL ENGINEERING , 2024, 42(6): 160-168. doi: 10.13205/j.hjgc.202406019
    [3]ZHOU Lei, LI Yalan, ZHANG Chaoqun, SONG Wen, YANG Kun, DU Mingyi, CHEN Qiang, LIU Yang. RESEARCH PROGRESS ON MONITORING AND SIMULATION OF SPATIAL DISTRIBUTION, VOLUME AND VARIATION OF CONSTRUCTION WASTE[J]. ENVIRONMENTAL ENGINEERING , 2024, 42(3): 243-253. doi: 10.13205/j.hjgc.202403030
    [4]XU Li, ZHOU Lawu, LI Gaojia. A RECYCLABLE WASTE SORTING SYSTEM BASED ON AN IMPROVED INCEPTION RESNET V2 NETWORK[J]. ENVIRONMENTAL ENGINEERING , 2024, 42(4): 233-241. doi: 10.13205/j.hjgc.202404027
    [5]LIU Zhi, GAO Dongming. APPLICATION AND COMPARISON OF DIFFERENT DEEP LEARNING MODELS IN RECOGNITION OF FOOD WASTE TYPES[J]. ENVIRONMENTAL ENGINEERING , 2024, 42(3): 254-260. doi: 10.13205/j.hjgc.202403031
    [6]LIN Yudao, TAO Tao, XIN Kunlun, PU Zhengheng, CHEN Lei. GRAPH DEEP LEARNING: APPLICATION ON SHORT-TERM WATER DEMAND FORECASTING FOR WATER DISTRIBUTION NETWORK[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(4): 149-153. doi: 10.13205/j.hjgc.202304021
    [7]LI Yuanyuan, LIU Hailong. PREDICTION OF TOTAL PHOSPHORUS IN RIVERS BASED ON ATTENTION MECHANISM OF TEMPORAL CONVOLUTIONAL NETWORKS[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(5): 163-171. doi: 10.13205/j.hjgc.202305022
    [8]ZENG Xiangji, YAN Feng, LI Yonggang, PAN Yan, YANG Jingya, TAN Xiangtian. MONITORING METHODS AND THEIR APPLICATION OF FLOWING WATER POLLUTION BASED ON INTELLIGENT VISION[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(11): 78-83,122. doi: 10.13205/j.hjgc.202311014
    [9]ZHOU Yi, XIONG Zhen, WU Mingming, LI Jin, CHEN Cong, GAO Fang, LIU Chang, HUANG Kai. DESIGN AND IMPLEMENTATION OF INTELLIGENT OPERATION MANAGEMENT PLATFORM FOR AN UNDERGROUND SEWAGE TREATMENT PLANT[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(11): 148-153. doi: 10.13205/j.hjgc.202311023
    [10]YUAN Hongchun, ZANG Tianqi. DETECTION OF UNDERWATER TRASH BASED ON Ghost-YOLOv5 AND ATTENTION MECHANISM[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(7): 214-221. doi: 10.13205/j.hjgc.202307029
    [11]BAO Zunsheng, XIONG Xiaoli, LIU Jicheng, XIN Lu, ZHANG Danyu, LI Shanqiang. EXPLORATION OF MANUAL FINE REGULATION OF CARBON SOURCE DOSAGE IN AN ADVANCED WASTEWATER TREATMENT PLANT[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(4): 137-142. doi: 10.13205/j.hjgc.202304019
    [12]HU Song, LIU Guohong, HE Ying, YAN Jiachen, CHEN Hanle, YAN Xiliang, YAN Bing. PREDICTION ON PHOTOELECTRIC CONVERSION EFFICIENCY OF ORGANIC PHOTOVOLTAIC MATERIALS USING END-TO-END DEEP LEARNING[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(6): 188-193. doi: 10.13205/j.hjgc.202206024
    [13]JIN Peiwei, YAO Yan, LIANG Xiaoyu, CAI Jinhui. OVERVIEW OF RESEARCHES ON MUNICIPAL SOLID WASTE IMAGE RECOGNITION[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(1): 196-206. doi: 10.13205/j.hjgc.202201029
    [14]WANG Wensheng, NIAN Chengxu, ZHANG Chao, YAN Rupeng, WU Xinquan, ZHANG Xinbo. DESIGN OF AUTOMATIC GARBAGE SORTING BIN FOR NON-RESIDENTIAL AREA BASED ON YOLO v5[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(3): 159-165. doi: 10.13205/j.hjgc.202203024
    [15]WANG Yiming, MA Zhenhua, YANG Mengqi, DONG Xin, ZENG Siyu. A HYBRID MODELING STRATEGY FOR CONTROL SIMULATOR OF URBAN DRAINAGE SYSTEMS BASED ON DATA-DRIVEN AND MECHANISM-DRIVEN METHOD[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(6): 204-211,225. doi: 10.13205/j.hjgc.202206026
    [16]DONG Hao, SUN Lin, OUYANG Feng. PREDICTION OF PM2.5 CONCENTRATION BASED ON INFORMER[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(6): 48-54,62. doi: 10.13205/j.hjgc.202206006
    [17]WU Yuxing, WANG Xiaodong, CHEN Ning, YANG Benliang, YAN Tingliang, HUANG Qing. FULL-SCALE STUDY OF AN INTELLIGENT CARBON DOSING CONTROL SYSTEM IN A TYPICAL URBAN WASTEWATER TREATMENT PLANT[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(6): 212-218,271. doi: 10.13205/j.hjgc.202206027
    [18]LU Yao, YANG Jie, SHAO Zhi-juan, ZHU Cong-cong. PM2.5 ROBUST PREDICTION BASED ON STAGED TEMPORAL ATTENTION NETWORK[J]. ENVIRONMENTAL ENGINEERING , 2021, 39(10): 93-100. doi: 10.13205/j.hjgc.202110013
    [19]HUANG Chun-tao, FAN Dong-ping, LU Ji-fu, LIAO Qi-feng. PREDICTION OF PM2.5 AND PM10 CONCENTRATION IN GUANGZHOU BASED ON DEEP LEARNING MODEL[J]. ENVIRONMENTAL ENGINEERING , 2021, 39(12): 135-140. doi: 10.13205/j.hjgc.202112020
    [20]YU Shen-ting, LIU Ping. LONG SHORT-TERM MEMORY-CONVOLUTION NEURAL NETWORK (LSTM-CNN) FOR PREDICTION OF PM2.5 CONCENTRATION IN BEIJING[J]. ENVIRONMENTAL ENGINEERING , 2020, 38(6): 176-180,66. doi: 10.13205/j.hjgc.202006029
  • Cited by

    Periodical cited type(3)

    1. 黄学平,辛攀,吴永明,吴留兴,邓觅,姚忠. 融合残差与VMD-TCN-BiLSTM混合网络的鄱阳湖总氮预测. 长江科学院院报. 2025(03): 59-67+75 .
    2. 李世忠,满奕,何正磊. 基于DNN-LSTM的造纸废水处理过程温室气体排放分析模型. 中国造纸. 2024(04): 170-176 .
    3. 陈亚松,邱勇,柳蒙蒙,刘萌萌,刘雪洁,田宇心,黄霞. 污水处理软测量仪表研究进展与应用. 工业仪表与自动化装置. 2024(03): 60-67+88 .

    Other cited types(3)

  • Created with Highcharts 5.0.7Amount of accessChart context menuAbstract Views, HTML Views, PDF Downloads StatisticsAbstract ViewsHTML ViewsPDF Downloads2024-052024-062024-072024-082024-092024-102024-112024-122025-012025-022025-032025-0401020304050
    Created with Highcharts 5.0.7Chart context menuAccess Class DistributionFULLTEXT: 10.7 %FULLTEXT: 10.7 %META: 85.7 %META: 85.7 %PDF: 3.7 %PDF: 3.7 %FULLTEXTMETAPDF
    Created with Highcharts 5.0.7Chart context menuAccess Area Distribution其他: 24.2 %其他: 24.2 %其他: 0.3 %其他: 0.3 %上海: 2.5 %上海: 2.5 %东莞: 1.7 %东莞: 1.7 %保定: 0.3 %保定: 0.3 %兰州: 0.6 %兰州: 0.6 %北京: 6.2 %北京: 6.2 %南京: 0.8 %南京: 0.8 %南宁: 1.4 %南宁: 1.4 %南昌: 0.6 %南昌: 0.6 %南通: 0.3 %南通: 0.3 %台州: 0.8 %台州: 0.8 %合肥: 0.8 %合肥: 0.8 %唐山: 0.6 %唐山: 0.6 %喀什: 1.4 %喀什: 1.4 %嘉兴: 0.3 %嘉兴: 0.3 %夏延: 0.3 %夏延: 0.3 %大同: 0.6 %大同: 0.6 %天津: 3.4 %天津: 3.4 %太原: 0.3 %太原: 0.3 %宁波: 0.3 %宁波: 0.3 %宜春: 0.8 %宜春: 0.8 %宣城: 0.3 %宣城: 0.3 %宿州: 0.3 %宿州: 0.3 %宿迁: 0.3 %宿迁: 0.3 %巴音郭楞: 0.3 %巴音郭楞: 0.3 %常州: 1.4 %常州: 1.4 %常德: 0.3 %常德: 0.3 %广州: 0.6 %广州: 0.6 %弗吉: 0.6 %弗吉: 0.6 %张家口: 2.5 %张家口: 2.5 %成都: 1.4 %成都: 1.4 %扬州: 2.8 %扬州: 2.8 %无锡: 1.4 %无锡: 1.4 %昆明: 0.8 %昆明: 0.8 %晋城: 0.3 %晋城: 0.3 %杭州: 4.2 %杭州: 4.2 %柳州: 0.3 %柳州: 0.3 %桂林: 0.8 %桂林: 0.8 %武汉: 1.7 %武汉: 1.7 %沈阳: 0.8 %沈阳: 0.8 %济宁: 0.3 %济宁: 0.3 %深圳: 0.3 %深圳: 0.3 %温州: 0.6 %温州: 0.6 %湖州: 2.2 %湖州: 2.2 %漯河: 3.7 %漯河: 3.7 %潜江: 0.3 %潜江: 0.3 %石家庄: 0.6 %石家庄: 0.6 %绵阳: 0.3 %绵阳: 0.3 %芒廷维尤: 9.0 %芒廷维尤: 9.0 %芝加哥: 3.9 %芝加哥: 3.9 %蒙哥马利: 0.6 %蒙哥马利: 0.6 %衡阳: 0.6 %衡阳: 0.6 %衢州: 0.6 %衢州: 0.6 %西宁: 3.7 %西宁: 3.7 %西安: 0.3 %西安: 0.3 %贵阳: 0.3 %贵阳: 0.3 %运城: 1.1 %运城: 1.1 %遵义: 0.3 %遵义: 0.3 %郑州: 0.3 %郑州: 0.3 %重庆: 0.8 %重庆: 0.8 %长沙: 0.6 %长沙: 0.6 %青岛: 0.6 %青岛: 0.6 %其他其他上海东莞保定兰州北京南京南宁南昌南通台州合肥唐山喀什嘉兴夏延大同天津太原宁波宜春宣城宿州宿迁巴音郭楞常州常德广州弗吉张家口成都扬州无锡昆明晋城杭州柳州桂林武汉沈阳济宁深圳温州湖州漯河潜江石家庄绵阳芒廷维尤芝加哥蒙哥马利衡阳衢州西宁西安贵阳运城遵义郑州重庆长沙青岛

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (303) PDF downloads(14) Cited by(6)
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return