Volume 12 Issue 6
Nov.  2022
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WEI X S,GAO H J,CHEN Y H,et al.Research progress of artificial intelligence technology in the field of water pollution control[J].Journal of Environmental Engineering Technology,2022,12(6):2057-2063 doi: 10.12153/j.issn.1674-991X.20210638
Citation: WEI X S,GAO H J,CHEN Y H,et al.Research progress of artificial intelligence technology in the field of water pollution control[J].Journal of Environmental Engineering Technology,2022,12(6):2057-2063 doi: 10.12153/j.issn.1674-991X.20210638

Research progress of artificial intelligence technology in the field of water pollution control

doi: 10.12153/j.issn.1674-991X.20210638
  • Received Date: 2021-11-04
    Available Online: 2022-04-21
  • Artificial intelligence (AI) technologies have great potential in the field of environmental engineering because of the unique performance of self-learning, self-adaptation and self-organization. At present, they have been widely used in the environmental fields such as water pollution, air pollution, solid waste treatment, climate change, which indicate that AI technologies are good assistants for environmental monitoring and governance. In the current situation of serious water resources shoutage, water pollution prevention and control is of great importance. Traditional water pollution control and supervision technologies have problems such as serious lag effect of water pollution monitoring, high cost of sewage optimization control, and low prediction accuracy of pollutant removal efficiency. The introduction of artificial intelligence technology can effectively overcome the above problems. It is of great significance to develop the application of AI in water pollution control. The characteristics and classification of various AI technologies were discussed, the research status and application progress of AI technologies in the field of water pollution control were summarized, in order to provide scientific reference for comprehensively strengthening water pollution control.

     

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  • [1]
    BARR A, FEIGENBAUM E A. The handbook of artificial intelligence[M]. Los Altos, CA: Morgan Kaufmann, 1981.
    [2]
    YE Z P, YANG J Q, ZHONG N, et al. Tackling environmental challenges in pollution controls using artificial intelligence: a review[J]. Science of the Total Environment,2020,699:134279. doi: 10.1016/j.scitotenv.2019.134279
    [3]
    HUNTINGFORD C, JEFFERS E S, BONSALL M B, et al. Machine learning and artificial intelligence to aid climate change research and preparedness[J]. Environmental Research Letters,2019,14(12):124007. doi: 10.1088/1748-9326/ab4e55
    [4]
    陈能汪, 余镒琦, 陈纪新, 等.人工神经网络模型在水质预警中的应用研究进展[J]. 环境科学学报,2021,41(12):4771-4782.

    CHEN N W, YU Y Q, CHEN J X, et al. Artificial neural network models for water quality early warning: a review[J]. Acta Scientiae Circumstantiae,2021,41(12):4771-4782.
    [5]
    PARK Y, KIM M, PACHEPSKY Y, et al. Development of a nowcasting system using machine learning approaches to predict fecal contamination levels at recreational beaches in Korea[J]. Journal of Environmental Quality,2018,47(5):1094-1102. doi: 10.2134/jeq2017.11.0425
    [6]
    WANG P, LIU Y, QIN Z D, et al. A novel hybrid forecasting model for PM10 and SO2 daily concentrations[J]. Science of the Total Environment,2015,505:1202-1212. doi: 10.1016/j.scitotenv.2014.10.078
    [7]
    PALANISWAMY D, RAMESH G, SIVASANKARAN S, et al. Optimising biogas from food waste using a neural network model[J]. Proceedings of the Institution of Civil Engineers-Municipal Engineer,2017,170(4):221-229. doi: 10.1680/jmuen.16.00008
    [8]
    KATIP A. Meteorological drought analysis using artificial neural networks for Bursa City, Turkey[J]. Applied Ecology and Environmental Research,2018,16(3):3315-3332. doi: 10.15666/aeer/1603_33153332
    [9]
    SHOOSHTARI S J, SILVA T, NAMIN B R, et al. Land use and cover change assessment and dynamic spatial modeling in the Ghara-su Basin, Northeastern Iran[J]. Journal of the Indian Society of Remote Sensing,2020,48(1):81-95. doi: 10.1007/s12524-019-01054-x
    [10]
    GOVINDARAJU R S. Artificial neural networks in hydrology: Ⅰ. preliminary concepts[J]. Journal of Hydrologic Engineering,2000,5(2):115-123. doi: 10.1061/(ASCE)1084-0699(2000)5:2(115)
    [11]
    OJHA V K, ABRAHAM A, SNÁŠEL V. Metaheuristic design of feedforward neural networks: a review of two decades of research[J]. Engineering Applications of Artificial Intelligence,2017,60:97-116. doi: 10.1016/j.engappai.2017.01.013
    [12]
    YIN Z Y, JIA B Y, WU S Q, et al. Comprehensive forecast of urban water-energy demand based on a neural network model[J]. Water,2018,10(4):385. doi: 10.3390/w10040385
    [13]
    JAMI M S, MUJELI M, KABBASHI N A. Simulation of ammoniacal nitrogen effluent using feedforward multilayer neural networks[J]. African Journal of Biotechnology,2011,81(10):18755-18762.
    [14]
    EBRAHIMPOOR S, KIAROSTAMI V, KHOSRAVI M, et al. Bees metaheuristic algorithm with the aid of artificial neural networks for optimization of acid red 27 dye adsorption onto novel polypyrrole/SrFe12O19/graphene oxide nanocomposite[J]. Polymer Bulletin,2019,76(12):6529-6553. doi: 10.1007/s00289-019-02700-7
    [15]
    YU R F, CHI F H, CHENG W P, et al. Application of pH, ORP, and DO monitoring to evaluate chromium(Ⅵ) removal from wastewater by the nanoscale zero-valent iron (nZVI) process[J]. Chemical Engineering Journal,2014,255:568-576. doi: 10.1016/j.cej.2014.06.002
    [16]
    BENSIDHOUM T, BOUAKRIF F, ZASADZINSKI M. Iterative learning radial basis function neural networks control for unknown multi input multi output nonlinear systems with unknown control direction[J]. Transactions of the Institute of Measurement and Control,2019,41(12):3452-3467. doi: 10.1177/0142331219826659
    [17]
    WANG J Y, SONG P Z, WANG Z, et al. A combined model for regional eco-environmental quality evaluation based on particle swarm optimization-radial basis function network[J]. Arabian Journal for Science and Engineering,2016,41(4):1483-1493. doi: 10.1007/s13369-015-1958-5
    [18]
    OZEL H U, GEMICI B T, OZEL H B, et al. Determination of water quality and estimation of monthly biological oxygen demand (BOD) using by different artificial neural networks models in the Bartin River[J]. Fresenius Environmental Bulletin,2017,26(8):5465-5476.
    [19]
    BOLANCA T, UKIC S, PETERNEL I, et al. Artificial neural network models for advanced oxidation of organics in water matrix-comparison of applied methodologies[J]. Indian Journal of Chemical Technology,2014,21(1):21-29.
    [20]
    ASFARAM A, GHAEDI M, AHMADI AZQHANDI M H, et al. Ultrasound-assisted binary adsorption of dyes onto Mn@CuS/ZnS-NC-AC as a novel adsorbent: application of chemometrics for optimization and modeling[J]. Journal of Industrial and Engineering Chemistry,2017,54:377-388. doi: 10.1016/j.jiec.2017.06.018
    [21]
    SINGH K P, GUPTA S, OJHA P, et al. Predicting adsorptive removal of chlorophenol from aqueous solution using artificial intelligence based modeling approaches[J]. Environmental Science and Pollution Research International,2013,20(4):2271-2287. doi: 10.1007/s11356-012-1102-y
    [22]
    TURAN N G, MESCI B, OZGONENEL O. The use of artificial neural networks (ANN) for modeling of adsorption of Cu(Ⅱ) from industrial leachate by pumice[J]. Chemical Engineering Journal,2011,171(3):1091-1097. doi: 10.1016/j.cej.2011.05.005
    [23]
    PAI P F, LIN K P, LIN C S, et al. Time series forecasting by a seasonal support vector regression model[J]. Expert Systems with Applications,2010,37(6):4261-4265. doi: 10.1016/j.eswa.2009.11.076
    [24]
    VAPNIK V N. The nature of statistical learning theory[M]. New York: Springer, 1995.
    [25]
    JARAMILLO F, ORCHARD M, MUÑOZ C, et al. On-line estimation of the aerobic phase length for partial nitrification processes in SBR based on features extraction and SVM classification[J]. Chemical Engineering Journal,2018,331:114-123. doi: 10.1016/j.cej.2017.07.185
    [26]
    HUANG J, ZHANG X, SUN Q Y, et al. Simultaneous rapid analysis of multiple nitrogen compounds in polluted river treatment using near-infrared spectroscopy and a support vector machine[J]. Polish Journal of Environmental Studies,2017,26(5):2013-2019. doi: 10.15244/pjoes/70002
    [27]
    GAO K, XI X J, WANG Z, et al. Use of support vector machine model to predict membrane permeate flux[J]. Desalination and Water Treatment,2016,57(36):16810-16821.
    [28]
    ZHANG J, ZHOU J T, LI Y M, et al. Computer simulating effluent quality of vertical tube biological reactor using support vector machine[J]. Advanced Materials Research,2011,219/220:322-326. doi: 10.4028/www.scientific.net/AMR.219-220.322
    [29]
    KALOGIROU S A. Artificial intelligence for the modeling and control of combustion processes: a review[J]. Progress in Energy and Combustion Science,2003,29(6):515-566. doi: 10.1016/S0360-1285(03)00058-3
    [30]
    AL-OBAIDI M A, LI J P, ALSADAIE S, et al. Modelling and optimisation of a multistage reverse osmosis processes with permeate reprocessing and recycling for the removal of N-nitrosodimethylamine from wastewater using Species Conserving Genetic Algorithms[J]. Chemical Engineering Journal,2018,350:824-834. doi: 10.1016/j.cej.2018.06.022
    [31]
    LOUZADAVALORY J P, REIS J A T D, MENDONÇA A S F. Combining genetic algorithms with a water quality model to determine efficiencies of sewage treatment systems in watersheds[J]. Journal of Environmental Engineering,2016,142(3):04015080. doi: 10.1061/(ASCE)EE.1943-7870.0001048
    [32]
    BRAND N, OSTFELD A. Optimal design of regional wastewater pipelines and treatment plant systems[J]. Water Environment Research,2011,83(1):53-64. doi: 10.2175/106143010X12780288628219
    [33]
    YETILMEZSOY K, OZKAYA B, CAKMAKCI M. Artificial Intelligence-based prediction models for environmental engineering[J]. Neural Network World,2011,21(3):193-218. doi: 10.14311/NNW.2011.21.012
    [34]
    de OLIVEIRA M D D, de REZENDE O L T d, OLIVEIRA S M A C, et al. Nova abordagem doíndice de qualidade deágua bruta utilizando a lógica fuzzy[J]. Engenharia Sanitaria e Ambiental,2014,19(4):361-372. doi: 10.1590/S1413-41522014019000000803
    [35]
    AL-ZAHRANI M, MOIED K. Identifying water quality monitoring stations in a water supply system[J]. Water Science and Technology:Water Supply,2014,14(6):1076-1086. doi: 10.2166/ws.2014.069
    [36]
    SARI H, YETILMEZSOY K, ILHAN F, et al. Fuzzy-logic modeling of Fenton's strong chemical oxidation process treating three types of landfill leachates[J]. Environmental Science and Pollution Research International,2013,20(6):4235-4253. doi: 10.1007/s11356-012-1370-6
    [37]
    LIU B, HUANG J J, MCBEAN E, et al. Risk assessment of hybrid rain harvesting system and other small drinking water supply systems by game theory and fuzzy logic modeling[J]. Science of the Total Environment,2020,708:134436. doi: 10.1016/j.scitotenv.2019.134436
    [38]
    de OLIVEIRA M D, de REZENDE O L T, de FONSECA J F R, et al. Evaluating the surface water quality index fuzzy and its influence on water treatment[J]. Journal of Water Process Engineering,2019,32:100890. doi: 10.1016/j.jwpe.2019.100890
    [39]
    FLORES-ASIS R, MÉNDEZ-CONTRERAS J M, ALVARADO-LASSMAN A, et al. Analysis of the behavior for operation parameters in the anaerobic digestion process with thermal pretreatment, using fuzzy logic[J]. Journal of Environmental Science and Health, Part A-Toxic/Hazardous Substances & Environmental Engineering,2019,54(6):592-602.
    [40]
    DOGDU G, YALCUK A, POSTALCIOGLU S. Application of the removal of pollutants from textile industry wastewater in constructed wetlands using fuzzy logic[J]. Environmental Technology,2017,38(4):443-455. doi: 10.1080/09593330.2016.1196741
    [41]
    SUTHAR S, VERMA R, DEEP S, et al. Optimization of conditions (pH and temperature) for Lemna gibba production using fuzzy model coupled with Mamdani ′s method[J]. Ecological Engineering,2015,83:452-455. doi: 10.1016/j.ecoleng.2015.07.006
    [42]
    RAHIMZADEH A, ASHTIANI F Z, OKHOVAT A. Application of adaptive neuro-fuzzy inference system as a reliable approach for prediction of oily wastewater microfiltration permeate volume[J]. Journal of Environmental Chemical Engineering,2016,4(1):576-584. doi: 10.1016/j.jece.2015.12.011
    [43]
    KIM C M, PARNICHKUN M. Prediction of settled water turbidity and optimal coagulant dosage in drinking water treatment plant using a hybrid model of k-means clustering and adaptive neuro-fuzzy inference system[J]. Applied Water Science,2017,7(7):3885-3902. doi: 10.1007/s13201-017-0541-5
    [44]
    TAN H M, POH P E, GOUWANDA D. Resolving stability issue of thermophilic high-rate anaerobic palm oil mill effluent treatment via adaptive neuro-fuzzy inference system predictive model[J]. Journal of Cleaner Production,2018,198:797-805. doi: 10.1016/j.jclepro.2018.07.027
    [45]
    NAJAFZADEH M, ZEINOLABEDINI M. Prognostication of waste water treatment plant performance using efficient soft computing models: an environmental evaluation[J]. Measurement,2019,138:690-701. doi: 10.1016/j.measurement.2019.02.014
    [46]
    SADI M, FAKHARIAN H, GANJI H, et al. Evolving artificial intelligence techniques to model the hydrate-based desalination process of produced water[J]. Journal of Water Reuse and Desalination,2019,9(4):372-384. doi: 10.2166/wrd.2019.024
    [47]
    HUANG M Z, MA Y W, WAN J Q, et al. Improving nitrogen removal using a fuzzy neural network-based control system in the anoxic/oxic process[J]. Environmental Science and Pollution Research International,2014,21(20):12074-12084. doi: 10.1007/s11356-014-3092-4
    [48]
    AZQHANDI M H A, FOROUGHI M, YAZDANKISH E. A highly effective, recyclable, and novel host-guest nanocomposite for Triclosan removal: a comprehensive modeling and optimization-based adsorption study[J]. Journal of Colloid and Interface Science,2019,551:195-207. doi: 10.1016/j.jcis.2019.05.007
    [49]
    GHAEDI M, ANSARI A, BAHARI F, et al. A hybrid artificial neural network and particle swarm optimization for prediction of removal of hazardous dye brilliant green from aqueous solution using zinc sulfide nanoparticle loaded on activated carbon[J]. Spectrochimica Acta Part A-Molecular and Biomolecular Spectroscopy,2015,137:1004-1015. doi: 10.1016/j.saa.2014.08.011
    [50]
    GHAEDI M, DASHTIAN K, GHAEDI A M, et al. A hybrid model of support vector regression with genetic algorithm for forecasting adsorption of malachite green onto multi-walled carbon nanotubes: central composite design optimization[J]. Physical Chemistry Chemical Physics,2016,18(19):13310-13321. doi: 10.1039/C6CP01531J
    [51]
    GHAEDI A M, GHAEDI M, POURANFARD A R, et al. Adsorption of Triamterene on multi-walled and single-walled carbon nanotubes: artificial neural network modeling and genetic algorithm optimization[J]. Journal of Molecular Liquids,2016,216:654-665. doi: 10.1016/j.molliq.2016.01.068
    [52]
    陈威, 陈会娟, 戴凡翔, 等.基于人工神经网络的污水处理出水水质预测模型[J]. 给水排水,2020,46(增刊1):990-994.

    CHEN W, CHEN H J, DAI F X, et al. Effluent water quality prediction model based on artificial neural network for wastewater treatment[J]. Water & Wastewater Engineering,2020,46(Suppl1):990-994.
    [53]
    GHAEDI A M, VAFAEI A. Applications of artificial neural networks for adsorption removal of dyes from aqueous solution: a review[J]. Advances in Colloid and Interface Science,2017,245:20-39. doi: 10.1016/j.cis.2017.04.015
    [54]
    PECHE R, RODRÍGUEZ E. Development of environmental quality indexes based on fuzzy logic: a case study[J]. Ecological Indicators,2012,23:555-565. doi: 10.1016/j.ecolind.2012.04.029
    [55]
    FAN M Y, HU J W, CAO R S, et al. A review on experimental design for pollutants removal in water treatment with the aid of artificial intelligence[J]. Chemosphere,2018,200:330-343. ⊗ doi: 10.1016/j.chemosphere.2018.02.111
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