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Improving total nitrogen removal using a neural network ammonia-based aeration control in activated sludge process


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Aguilar-López, R., López-Pérez, P. A. and Neria-González, M. I. 2016. Improvement of activated sludge process using a nonlinear pi controller design via adaptive gain. International Journal of Chemical Reactor Engineering 14(1): 407–416.Aguilar-LópezR.López-PérezP. A.Neria-GonzálezM. I.2016Improvement of activated sludge process using a nonlinear pi controller design via adaptive gainInternational Journal of Chemical Reactor Engineering14(1):40741610.1515/ijcre-2014-0129Search in Google Scholar

Alex, J., Benedetti, L., Copp, J., Gernaey, K. V., Jeppsson, U., Nopens, I. and Winkler, S. 2008. Benchmark Simulation Model No. 1 (BSM1).AlexJ.BenedettiL.CoppJ.GernaeyK. V.JeppssonU.NopensI.WinklerS.2008Benchmark Simulation Model No. 1 (BSM1)Search in Google Scholar

Åmand, L. and Carlsson, B. 2013. The optimal dissolved oxygen profile in a nitrifying activated sludge process – comparisons with ammonium feedback control. Water Science and Technology 68(3): 641–649.ÅmandL.CarlssonB.2013The optimal dissolved oxygen profile in a nitrifying activated sludge process – comparisons with ammonium feedback controlWater Science and Technology68(3):64164910.2166/wst.2013.28723925193Search in Google Scholar

Åmand, L., Olsson, G. and Carlsson, B. 2013. Aeration control – a review. Water Science and Technology: A Journal of the International Association on Water Pollution Research 67(11): 2374–2398.ÅmandL.OlssonG.CarlssonB.2013Aeration control – a reviewWater Science and Technology: A Journal of the International Association on Water Pollution Research67(11):2374239810.2166/wst.2013.13923752368Search in Google Scholar

Åmand, L. and Carlsson, B. 2014. Aeration control with gain scheduling in a full-scale wastewater treatment plant. IFAC Proceedings Volumes (IFAC-PapersOnline)(Vol. 19), IFAC, available at: http://dx.doi.org/10.3182/20140824-6-ZA-1003.01892.ÅmandL.CarlssonB.2014Aeration control with gain scheduling in a full-scale wastewater treatment plant. IFAC Proceedings Volumes (IFAC-PapersOnline)(Vol. 19), IFAC, available at:http://dx.doi.org/10.3182/20140824-6-ZA-1003.0189210.3182/20140824-6-ZA-1003.01892Search in Google Scholar

Arnell, M. 2016. Performance assessment of wastewater treatment plants multi-objective analysis using plant-wide models.ArnellM.2016Performance assessment of wastewater treatment plants multi-objective analysis using plant-wide modelsSearch in Google Scholar

Cristea, S., de Prada, C., Sarabia, D. and Gutiérrez, G. 2011. Aeration control of a wastewater treatment plant using hybrid NMPC. Computers & Chemical Engineering 35(4): 638–650.CristeaS.de PradaC.SarabiaD.GutiérrezG.2011Aeration control of a wastewater treatment plant using hybrid NMPCComputers & Chemical Engineering35(4):63865010.1016/j.compchemeng.2010.07.021Search in Google Scholar

Du, X., Wang, J., Jegatheesan, V. and Shi, G. 2018. Dissolved oxygen control in activated sludge process using a neural network-based adaptive PID algorithm. Applied Sciences 8(2): 261.DuX.WangJ.JegatheesanV.ShiG.2018Dissolved oxygen control in activated sludge process using a neural network-based adaptive PID algorithmApplied Sciences8(2):26110.3390/app8020261Search in Google Scholar

Ellis, T. G., Eliosov, B., Schmit, C. G., Jahan, K. and Park, K. Y. 2009. Activated Sludge and other Aerobic Suspended Culture Processes. Water Environment Research: a Research Publication of the Water Environment Federation, Vol. 74, available at: http://www.ncbi.nlm.nih.gov/pubmed/12413140.EllisT. G.EliosovB.SchmitC. G.JahanK.ParkK. Y.2009Activated Sludge and other Aerobic Suspended Culture ProcessesWater Environment Research: a Research Publication of the Water Environment FederationVol.74available at: http://www.ncbi.nlm.nih.gov/pubmed/12413140Search in Google Scholar

Fazelabdolabadi, B., Montazeri, M. and Pourafshary, P. 2021. HighTech and innovation a data mining perspective on the confluent ions. Effect for Target Functionality 2(3): 202–215.FazelabdolabadiB.MontazeriM.PourafsharyP.2021HighTech and innovation a data mining perspective on the confluent ionsEffect for Target Functionality2(3):202215Search in Google Scholar

Ghoneim, W. A. M., Helal, A. A. and Wahab, M. G. A. 2016. Minimizing energy consumption in Wastewater Treatment Plants. 2016 3rd International Conference on Renewable Energies for Developing Countries, REDEC 2016, Institute of Electrical and Electronics Engineers Inc, available at: https://doi.org/10.1109/REDEC.2016.7577507GhoneimW. A. M.HelalA. A.WahabM. G. A.2016Minimizing energy consumption in Wastewater Treatment Plants2016 3rd International Conference on Renewable Energies for Developing Countries, REDEC 2016, Institute of Electrical and Electronics Engineers Incavailable at:https://doi.org/10.1109/REDEC.2016.757750710.1109/REDEC.2016.7577507Search in Google Scholar

Han, H. and Qiao, J. 2014. Nonlinear model-predictive control for industrial processes: an application to wastewater treatment process. IEEE Transactions on Industrial Electronics 61(4): 1970–1982.HanH.QiaoJ.2014Nonlinear model-predictive control for industrial processes: an application to wastewater treatment processIEEE Transactions on Industrial Electronics61(4):1970198210.1109/TIE.2013.2266086Search in Google Scholar

Han, H. -G., Qiao, J. -F. and Chen, Q. -L. 2012. Model predictive control of dissolved oxygen concentration based on a self-organizing RBF neural network. Control Engineering Practice 20(4): 465–476.HanH. -G.QiaoJ. -F.ChenQ. -L.2012Model predictive control of dissolved oxygen concentration based on a self-organizing RBF neural networkControl Engineering Practice20(4):46547610.1016/j.conengprac.2012.01.001Search in Google Scholar

Han, H., Liu, Z., Hou, Y. and Qiao, J. 2020. Data-driven multiobjective predictive control for wastewater treatment process, IEEE Transactions on Industrial Informatics 16(4): 1.HanH.LiuZ.HouY.QiaoJ.2020Data-driven multiobjective predictive control for wastewater treatment processIEEE Transactions on Industrial Informatics16(4):110.1109/TII.2019.2940663Search in Google Scholar

Holenda, B., Domokos, E. and Fazakas, J. 2008. Dissolved oxygen control of the activated sludge wastewater treatment process using model predictive control. Computer and Chemical Engineering 32: 1270–1278.HolendaB.DomokosE.FazakasJ.2008Dissolved oxygen control of the activated sludge wastewater treatment process using model predictive controlComputer and Chemical Engineering321270127810.1016/j.compchemeng.2007.06.008Search in Google Scholar

Huang, G. B. 2003. Learning capability and storage capacity of two-hidden-layer feedforward networks. IEEE Transactions on Neural Networks 14(2): 274–281.HuangG. B.2003Learning capability and storage capacity of two-hidden-layer feedforward networksIEEE Transactions on Neural Networks14(2):27428110.1109/TNN.2003.809401Search in Google Scholar

Husin, M. H., Rahmat, M. F. and Wahab, N. A. 2020a. Decentralized proportional-integral control with carbon addition for wastewater treatment plant. Bulletin of Electrical Engineering and Informatics 9(6): 2278–2285.HusinM. H.RahmatM. F.WahabN. A.2020aDecentralized proportional-integral control with carbon addition for wastewater treatment plantBulletin of Electrical Engineering and Informatics9(6):2278228510.11591/eei.v9i6.2170Search in Google Scholar

Husin, M. H., Rahmat, M. F., Wahab, N. A., Sabri, M. F. M. and Suhaili, S. 2020b. Proportional-integral ammonium-based aeration control for activated sludge process. 2020 13th International UNIMAS Engineering Conference (EnCon), IEEE, pp. 1–5, available at: https://ieeexplore.ieee.org/document/9299339/.HusinM. H.RahmatM. F.WahabN. A.SabriM. F. M.SuhailiS.2020bProportional-integral ammonium-based aeration control for activated sludge process2020 13th International UNIMAS Engineering Conference (EnCon), IEEEpp.15available at: https://ieeexplore.ieee.org/document/9299339/10.1109/EnCon51501.2020.9299339Search in Google Scholar

Husin, M. H., Rahmat, M. F., Wahab, N. A. and Sabri, M. F. M. 2021a. Neural network ammonia-based aeration control for activated sludge process wastewater treatment plant. Lecture Notes in Electrical Engineering 666: 471–487.HusinM. H.RahmatM. F.WahabN. A.SabriM. F. M.2021aNeural network ammonia-based aeration control for activated sludge process wastewater treatment plantLecture Notes in Electrical Engineering66647148710.1007/978-981-15-5281-6_32Search in Google Scholar

Husin, M. H., Rahmat, M. F., Wahab, N. A., Sabri, M. F. M. and Md Zain, Z. 2021b. Neural network ammonia-based aeration control for activated sludge process wastewater treatment plant. In Md Zain, Z. et al. (ed.), Proceedings of the 11th National Technical Seminar on Unmanned System Technology 2019. Lecture Notes in Electrical Engineering Vol. 666: 471–487, available at: http://link.springer.com/10.1007/978-981-15-5281-6_32.HusinM. H.RahmatM. F.WahabN. A.SabriM. F. M.andMd ZainZ.2021bNeural network ammonia-based aeration control for activated sludge process wastewater treatment plant. In Md Zain, Z. et al. (ed.),Proceedings of the 11th National Technical Seminar on Unmanned System Technology 2019Lecture Notes in Electrical EngineeringVol. 666471487available at:http://link.springer.com/10.1007/978-981-15-5281-6_3210.1007/978-981-15-5281-6_32Search in Google Scholar

Jinchuan, K. and Xinzhe, L. 2008. Empirical analysis of optimal hidden neurons in neural network modeling for stock prediction. Proceedings – 2008 Pacific-Asia Workshop on Computational Intelligence and Industrial Application, PACIIA 2008, 2: 828–832.JinchuanK.XinzheL.2008Empirical analysis of optimal hidden neurons in neural network modeling for stock predictionProceedings – 2008 Pacific-Asia Workshop on Computational Intelligence and Industrial Application, PACIIA 20082828832Search in Google Scholar

Martín, J. M., Vega, P., Informática, D. and Revollar, S. 2012. Set-point Optimization for Enhancing the MPC Control of the N-Removal Process in WWTP’s, pp. 1–6.MartínJ. M.VegaP.InformáticaD.RevollarS.2012Set-point Optimization for Enhancing the MPC Control of the N-Removal Process in WWTP’spp.16Search in Google Scholar

Medinilla, V. R., Sprague, T., Marseilles, J., Burke, J., Deshmukh, S., Delagah, S. and Sharbatmaleki, M. 2020. Impact of Ammonia-Based Aeration Control (ABAC) on energy consumption. Applied Sciences 10(15): 5227.MedinillaV. R.SpragueT.MarseillesJ.BurkeJ.DeshmukhS.DelagahS.SharbatmalekiM.2020Impact of Ammonia-Based Aeration Control (ABAC) on energy consumptionApplied Sciences10(15):522710.3390/app10155227Search in Google Scholar

Møller, M. F. 1993. A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks 6(4): 525–533.MøllerM. F.1993A scaled conjugate gradient algorithm for fast supervised learningNeural Networks6(4):52553310.1016/S0893-6080(05)80056-5Search in Google Scholar

Olsson, G., Carlsson, B., Comas, J., Copp, J., Gernaey, K. V., Ingildsen, P. and Amand, L. 2014. Instrumentation, control and automation in wastewater – from London 1973 to Narbonne 2013. Water Science and Technology: A Journal of the International Association on Water Pollution Research 69(7): 1373–1385.OlssonG.CarlssonB.ComasJ.CoppJ.GernaeyK. V.IngildsenP.AmandL.2014Instrumentation, control and automation in wastewater – from London 1973 to Narbonne 2013Water Science and Technology: A Journal of the International Association on Water Pollution Research69(7):1373138510.2166/wst.2014.05724718326Search in Google Scholar

Qiao, J. F., Han, G. and Han, H. G. 2014. Neural network on-line modeling and controlling method for multi-variable control of wastewater treatment processes. Asian Journal of Control 16(4): 1213–1223.QiaoJ. F.HanG.HanH. G.2014Neural network on-line modeling and controlling method for multi-variable control of wastewater treatment processesAsian Journal of Control16(4):1213122310.1002/asjc.758Search in Google Scholar

Samsudin, S. I., Rahmat, M. F., Wahab, N. A., Gaya, M. S. and Razali, M. C. 2013. Application of adaptive decentralized PI controller for activated sludge process. Proceedings of the 2013 IEEE 8th Conference on Industrial Electronics and Applications, ICIEA 2013, 1792–1797.SamsudinS. I.RahmatM. F.WahabN. A.GayaM. S.RazaliM. C.2013Application of adaptive decentralized PI controller for activated sludge processProceedings of the 2013 IEEE 8th Conference on Industrial Electronics and Applications, ICIEA 20131792179710.1109/ICIEA.2013.6566659Search in Google Scholar

Samsudin, S. I., Rahmat, M. F., Wahab, N. A., Razali, M. C., Gaya, M. S. and Salim, S. N. S. 2014. Improvement of Activated Sludge Process Using Enhanced Nonlinear PI Controller. Arabian Journal for Science and Engineering 39: 6575–6586.SamsudinS. I.RahmatM. F.WahabN. A.RazaliM. C.GayaM. S.SalimS. N. S.2014Improvement of Activated Sludge Process Using Enhanced Nonlinear PI ControllerArabian Journal for Science and Engineering396575658610.1007/s13369-014-1285-2Search in Google Scholar

Santin, I., Pedret, C., Meneses, M. and Vilanova, R. 2015b. Process based control architecture for avoiding effluent pollutants quality limits violations in wastewater treatment plants. 2015 19th International Conference on System Theory, Control and Computing (ICSTCC), IEEE, pp. 396–402, available at: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7321326.SantinI.PedretC.MenesesM.VilanovaR.2015bProcess based control architecture for avoiding effluent pollutants quality limits violations in wastewater treatment plants2015 19th International Conference on System Theory, Control and Computing (ICSTCC), IEEEpp.396402available at:http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=732132610.1109/ICSTCC.2015.7321326Search in Google Scholar

Santín, I., Pedret, C. and Vilanova, R. 2015a. Fuzzy control and model predictive control configurations for effluent violations removal in wastewater treatment plants. Industrial and Engineering Chemistry Research 54(10): 2763–2775.SantínI.PedretC.VilanovaR.2015aFuzzy control and model predictive control configurations for effluent violations removal in wastewater treatment plantsIndustrial and Engineering Chemistry Research54(10):2763277510.1021/ie504079qSearch in Google Scholar

Santín, I., Pedret, C., Vilanova, R. and Meneses, M. 2016. Advanced decision control system for effluent violations removal in wastewater treatment plants. Control Engineering Practice 49(January): 60–75.SantínI.PedretC.VilanovaR.MenesesM.2016Advanced decision control system for effluent violations removal in wastewater treatment plantsControl Engineering Practice49(January):607510.1016/j.conengprac.2016.01.005Search in Google Scholar

Sarabu, A. and Santra, A. K. 2021. Human action recognition in videos using convolution long short-term memory network with spatio-temporal networks. Emerging Science Journal 5(1): 25–33.SarabuA.SantraA. K.2021Human action recognition in videos using convolution long short-term memory network with spatio-temporal networksEmerging Science Journal5(1):253310.28991/esj-2021-01254Search in Google Scholar

Shibata, K. and Ikeda, Y. 2009. Effect of number of hidden neurons on learning in large-scale layered neural networks. ICCAS-SICE 2009 – ICROS-SICE International Joint Conference 2009, Proceedings, pp. 5008–5013.ShibataK.IkedaY.2009Effect of number of hidden neurons on learning in large-scale layered neural networksICCAS-SICE 2009 – ICROS-SICE International Joint Conference 2009, Proceedingspp.50085013Search in Google Scholar

Uprety, K., Kennedy, A., Balzer, W., Baumler, R., Duke, R. and Bott, C. 2015. Implementation of ammonia-based aeration control (ABAC) at full-scale wastewater treatment plants. Proceedings of the Water Environment Federation 2015(3): 1–10.UpretyK.KennedyA.BalzerW.BaumlerR.DukeR.BottC.2015Implementation of ammonia-based aeration control (ABAC) at full-scale wastewater treatment plantsProceedings of the Water Environment Federation2015(3):11010.2175/193864715819557902Search in Google Scholar

Várhelyi, M., Brehar, M. and Cristea, V. M. 2018. Control strategies for wastewater treatment plants aimed to improve nutrient removal and to reduce aeration costs. 2018 IEEE International Conference on Automation, Quality and Testing, Robotics, AQTR 2018 - THETA 21st Edition, Proceedings, pp. 1–6, available at: https://doi.org/10.1109/AQTR.2018.8402750.VárhelyiM.BreharM.CristeaV. M.2018Control strategies for wastewater treatment plants aimed to improve nutrient removal and to reduce aeration costs2018 IEEE International Conference on Automation, Quality and Testing, Robotics, AQTR 2018 - THETA 21st Edition, Proceedingspp.16available at:https://doi.org/10.1109/AQTR.2018.840275010.1109/AQTR.2018.8402750Search in Google Scholar

Vrečko, D. and Hvala, N. 2013. Model-based control of the ammonia nitrogen removal process in a wastewater treatment plant. S. Strmčnik & Đ. Juričić (Eds), London: Springer London, p. 417, available at: http://link.springer.com/10.1007/978-1-4471-5176-0.VrečkoD.HvalaN.2013Model-based control of the ammonia nitrogen removal process in a wastewater treatment plantStrmčnikS.JuričićĐ.(Eds)London: Springer Londonp.417available at:http://link.springer.com/10.1007/978-1-4471-5176-0Search in Google Scholar

Yang, T., Zhang, L., Wang, A. and Gao, H. 2013. Fuzzy modeling approach to predictions of chemical oxygen demand in activated sludge processes. Information Sciences 235: 55–64.YangT.ZhangL.WangA.GaoH.2013Fuzzy modeling approach to predictions of chemical oxygen demand in activated sludge processesInformation Sciences235556410.1016/j.ins.2012.07.021Search in Google Scholar

Yu, H. and Wilamowski, B. M. 2011. Levenberg–Marquardt training. Industrial Electronics Handbook Intelligent Systems. CRC Press, Boca Raton, pp. 12–15, available at: https://doi.org/10.1201/9781315218427.YuH.WilamowskiB. M.2011Levenberg–Marquardt trainingIndustrial Electronics Handbook Intelligent SystemsCRC Press, Boca Raton, pp.1215available at:https://doi.org/10.1201/978131521842710.1201/9781315218427Search in Google Scholar

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