Deep learning-based intelligent management for sewage treatment plants

Abstract

It is generally believed that intelligent management for sewage treatment plants (STPs) is essential to the sustainable engineering of future smart cities. The core of management lies in the precise prediction of daily volumes of sewage. The generation of sewage is the result of multiple factors from the whole social system. Characterized by strong process abstraction ability, data mining techniques have been viewed as promising prediction methods to realize intelligent STP management. However, existing data mining-based methods for this purpose just focus on a single factor such as an economical or meteorological factor and ignore their collaborative effects. To address this challenge, a deep learning-based intelligent management mechanism for STPs is proposed, to predict business volume. Specifically, the grey relation algorithm (GRA) and gated recursive unit network (GRU) are combined into a prediction model (GRA-GRU). The GRA is utilized to select the factors that have a significant impact on the sewage business volume, and the GRU is set up to output the prediction results. We conducted a large number of experiments to verify the efficiency of the proposed GRA-GRU model.

Keywords

deep learning / intelligent management / sewage treatment plants / grey relation algorithm / gated recursive unit

References

[1] ZhangQ, LiZ. Predictive models for wastewater flow forecasting based on time series analysis and artificial network [J]. Water Science and Technology, 2019, 80(2): 243-53

[2] WangS, MaC, ZhuY, et al.. Deep dewatering process of sludge by chemical conditioning and its potential influence on wastewater treatment plants [J]. Environmental Science and Pollution Research International, 2019, 26(33): 33838-33846

[3] GUO Z, SHEN Y, BASHIR A K, et al. Graph Embedding-based Intelligent Industrial Decision for Complex Sewage Treatment Processes [J]. International Journal of Intelligent Systems, 2021. DOI: https://doi.org/10.1002/int.22540.

[4] DingL, LvZ, HanM, et al.. Forecasting China’ wastewater discharge using dynamic factors and mixed-frequency data [J]. Environmental Pollution, 2019, 255: 113148

[5] ZhaoX, HanM, DingL, et al.. Usefulness of economic and energy data at different frequencies for carbon price forecasting in the EU ETS [J]. Applied Energy, 2018, 216132-141

[6] ZhaoX, HanM, DingL, et al.. Forecasting carbon dioxide emissions based on a hybrid of mixed data sampling regression model and back propagation neural network in the USA [J]. Environmental Science and Pollution Research International, 2018, 25(3): 2899-2910

[7] ZhangY, ZhaoT, ZhouA, et al.. Scenario based municipal wastewater estimation: Development and application of a dynamic simulation model [J]. Modelling and Simulation in Engineering, 2016, 2016: 1746310

[8] GuoZ, ShenY, AloqailyM, et al.. Probabilistic inference-based modeling for sustainable environmental systems under hybrid cloud infrastructure [J]. Simulation Modelling Practice and Theory, 2021, 107102215

[9] GiesslerM, TräncknerJ. A combined model to assess technical and economic consequences of changing conditions and management options for wastewater utilities [J]. Journal of Environmental Management, 2018, 20751-59

[10] WangZ, ShaoD, WesterhoffP. Wastewater discharge impact on drinking water sources along the Yangtze River (China) [J]. Science of the Total Environment, 2017, 599–600: 1399-1407

[11] Hernández-Del-OlmoF, GaudiosoE, DuroN, et al.. Machine learning weather soft-sensor for advanced control of wastewater treatment plants [J]. Sensors (Basel, Switzerland), 2019, 19(14): 3139

[12] ArlosM J, ParkerW J, BicudoJ R, et al.. Multi-year prediction of estrogenicity in municipal wastewater effluents [J]. The Science of the Total Environment, 2018, 610–611: 1103-1112

[13] ZhangA, YinX, LiuS, et al.. Distributed economic model predictive control of wastewater treatment plants [J]. Chemical Engineering Research and Design, 2019, 141144-155

[14] NavaresR, AznarteJ L. Forecasting Plantago pollen: Improving feature selection through random forests, clustering, and Friedman tests [J]. Theoretical and Applied Climatology, 2020, 139(12): 163-174

[15] LI Zhen-hua, ZOU Zhi-hong, YU Yang. Forecasting of wastewater discharge and the energy consumption in China based on grey model [J]. Mathematical Problems in Engineering, 2019: 4141085. DOI: https://doi.org/10.1155/2019/4141085.

[16] WangZ, HipelK W, WangQ, et al.. An optimized NGBM(1, 1) model for forecasting the qualified discharge rate of industrial wastewater in China [J]. Applied Mathematical Modelling, 2011, 35(12): 5524-5532

[17] WangZ, ManY, HuY, et al.. A deep learning based dynamic COD prediction model for urban sewage [J]. Environmental Science: Water Research & Technology, 2019, 5(12): 2210-2218

[18] GuoZ, WangH. A deep graph neural network-based mechanism for social recommendations [J]. IEEE Transactions on Industrial Informatics, 2021, 17(4): 2776-2783

[19] El-DinA G, SmithD W. A neural network model to predict the wastewater inflow incorporating rainfall events [J]. Water Research, 2002, 36(5): 1115-1126

[20] KokiC, MeligkotsidouL, VrontosI. Forecasting under model uncertainty: Non-homogeneous hidden Markov models with Pòlya-Gamma data augmentation [J]. Journal of Forecasting, 2020, 39(4): 580-598

[21] ZengB, DuanH, ZhouY. A new multivariable grey prediction model with structure compatibility [J]. Applied Mathematical Modelling, 2019, 75: 385-397

[22] QuJ, WangH, WangK, et al.. Municipal wastewater treatment in China: Development history and future perspectives [J]. Frontiers of Environmental Science & Engineering, 2019, 13(6): 1-7

[23] WuD, ZhouP, ZhouC Q. Evaluation of pulverized coal utilization in a blast furnace by numerical simulation and grey relational analysis [J]. Applied Energy, 2019, 250: 1686-1695

[24] YangM, LiuS, ChenK, et al.. A hierarchical clustering approach to fuzzy semantic representation of rare words in neural machine translation [J]. IEEE Transactions on Fuzzy Systems, 2020, 28(5): 992-1002

[25] WangY, LiuM, BaoZ, et al.. Short-term load forecasting with multi-source data using gated recurrent unit neural networks [J]. Energies, 2018, 11(5): 1138

[26] ZhangD, LindholmG, RatnaweeraH. Use long short-term memory to enhance Internet of Things for combined sewer overflow monitoring [J]. Journal of Hydrology, 2018, 556409-418

[27] VlachasP R, ByeonW, WanZ Y, et al.. Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks [J]. Proceedings Mathematical, Physical, and Engineering Sciences, 2018, 474(2213): 20170844

[28] JaouediN, BoujnahN, BouhlelM. Deep learning approach for human action recognition using gated recurrent unit neural networks and motion analysis [J]. Journal of Computer Science, 2019, 1571040-1049

[29] MaX, DaiZ, HeZ, et al.. Learning traffic as images: A deep convolutional neural network for large-scale transportation network speed prediction [J]. Sensors (Basel, Switzerland), 2017, 17(4): 818

[30] ZhangF, LiD. Multiple linear regression-structural equation modeling based development of the integrated model of perceived neighborhood environment and quality of life of community-dwelling older adults: A cross-sectional study in Nanjing, China [J]. International Journal of Environmental Research and Public Health, 2019, 16244933

[31] XU Yong, KONG Qiu-qiang, HUANG Qiang, et al. Convolutional gated recurrent neural network incorporating spatial features for audio tagging [C]//2017 International Joint Conference on Neural Networks (IJCNN). May 14–19, 2017, Anchorage, AK, USA. IEEE, 2017: 3461–3466. DOI: https://doi.org/10.1109/IJCNN.2017.7966291.

[32] HuangY, ShenL, LiuH. Grey relational analysis, principal component analysis and forecasting of carbon emissions based on long short-term memory in China [J]. Journal of Cleaner Production, 2019, 209415-423

[33] SuJ, LiC, ZengQ, et al.. A green closed-loop supply chain coordination mechanism based on third-party recycling [J]. Sustainability, 2019, 11195335

[34] ZhangL, ZhangR, WangZ, et al.. Spatial heterogeneity of the impact factors on gray water footprint intensity in China [J]. Sustainability, 2020, 123865

[35] ZhangX, HuangJ, LinL, et al.. Research on standards and regulations of the operation of wastewater treatment plants [J]. IOP Conference Series: Earth and Environmental Science, 2019, 2673032028

[36] GUO Z, YU K, JOLFAEI A, BASHIR A K, et al. A Fuzzy Detection System for Rumors through Explainable Adaptive Learning [J]. IEEE Transactions on Fuzzy Systems, 2021. DOI: https://doi.org/10.1109/TFUZZ.2021.3052109.