[1] Habib R Z, Thiemann T, Al Kendi R. Microplastics and wastewater treatment plants—A review. Journal of Water Resource and Protection, 2020, 12(1): 1−35 doi: 10.4236/jwarp.2020.121001
[2] 生态环境部. 2017中国生态环境状况公报 [Online], 获取自: http://www.mee.gov.cn/xxgk2018/xxgk/xxgk15/201912/t20191231_754132.html, 2020年7月10日
[3] Lu J Y, Wang X M, Liu H Q, Yu H Q, Li W W. Optimizing operation of municipal wastewater treatment plants in China: The remaining barriers and future implications. Environment International, 2019, 129: 273−278 doi: 10.1016/j.envint.2019.05.057
[4] Ben W W, Zhu B, Yuan X J, Zhang Y, Yang M, Qiang Z M. Occurrence, removal and risk of organic micropollutants in wastewater treatment plants across China: Comparison of wastewater treatment processes. Water Research, 2018, 130: 38−46 doi: 10.1016/j.watres.2017.11.057
[5] He Y, Zhu Y S, Chen J H, Huang M S, Wang P, Wang G H, et al. Assessment of energy consumption of municipal wastewater treatment plants in China. Journal of Cleaner Production, 2019, 228: 399−404 doi: 10.1016/j.jclepro.2019.04.320
[6] 张安龙, 谢飞, 罗清, 王先宝, 马蕊, 程丙军, 等. 中国城镇污水处理厂节能降耗研究进展. 环境科学与技术, 2018, 41(S1): 116−119

Zhang An-Long, Xie Fei, Luo Qing, Wang Xian-Bao, Ma Rui, Cheng Bing-Jun, et al. Research progress on energy saving and consumption reduction of urban sewage treatment plant in China. Environmental Science & Technology, 2018, 41(S1): 116−119
[7] 乔俊飞, 薄迎春, 韩广. 基于ESN的多指标DHP控制策略在污水处理过程中的应用. 自动化学报, 2013, 39(7): 1146−1151

Qiao Jun-Fei, Bo Ying-Chun, Han Guang. Application of ESN-based multi indices dual heuristic dynamic programming on wastewater treatment process. Acta Automatica Sinica, 2013, 39(7): 1146−1151
[8] Longo S, Hospido A, Lema J M, Mauricio-Iglesias M. A systematic methodology for the robust quantification of energy efficiency at wastewater treatment plants featuring data envelopment analysis. Water Research, 2018, 141: 317−328 doi: 10.1016/j.watres.2018.04.067
[9] 水资源-能源关系: 机遇与挑战 [Online], 获取自: http://www.tanpaifang.com/tanguwen/2014/0619/33887.html, 2020年7月10日
[10] 能源战略2050. [Online], 获取自: https://www.docin.com/p-1690461385.html, 2020年7月10日
[11] Rivera-Jaimes J A, Postigo C, Melgoza-Alemán R M, Aceña J, Barceló D, de Alda M L. Study of pharmaceuticals in surface and wastewater from Cuernavaca, Morelos, Mexico: Occurrence and environmental risk assessment. Science of the Total Environment, 2018, 613−614: 1263−1274 doi: 10.1016/j.scitotenv.2017.09.134
[12] Hreiz R, Latifi M A, Roche N. Optimal design and operation of activated sludge processes: State-of-the-art. Chemical Engineering Journal, 2015, 281: 900−920 doi: 10.1016/j.cej.2015.06.125
[13] Ostace G S, Baeza J A, Guerrero J, Guisasola A, Cristea V M, Agachi P Ş, et al. Development and economic assessment of different WWTP control strategies for optimal simultaneous removal of carbon, nitrogen and phosphorus. Computers & Chemical Engineering, 2013, 53: 164−177
[14] Han H G, Liu Z, Hou Y, Qiao J F. Data-driven multiobjective predictive control for wastewater treatment process. IEEE Transactions on Industrial Informatics, 2020, 16(4): 2767−2775 doi: 10.1109/TII.2019.2940663
[15] 彭永臻, 郭建华. 活性污泥膨胀机理、成因及控制. 北京: 科学出版社, 2012. 62−73

Peng Yong-Zhen, Guo Jian-Hua. Mechanism, Cause and Control of Activated Sludge Bulking. Beijing: Science Press, 2012. 62−73
[16] Gómez-Brandón M, Podmirseg S M. Biological waste treatment. Waste Management & Research: The Journal for A Sustainable Circular Economy, 2013, 31(8): 773−774
[17] Balakrishnan S, Eckenfelder W W. Nitrogen relationships in biological treatment processes-IIII. Denitrification in the modified activated sludge process. Water Research, 1969, 3(3): 177−178, IN1, 179-188 doi: 10.1016/0043-1354(69)90057-8
[18] Rosen C, Lennox J A. Multivariate and multiscale monitoring of wastewater treatment operation. Water Research, 2001, 35(14): 3402−3410 doi: 10.1016/S0043-1354(01)00069-0
[19] Henze M, Gujer W, Mino T, Matsuo T, Wentzel M C, Marais G R, et al. Activated sludge model no.2D, ASM2D. Water science and Technology, 1999, 39(1): 165−182 doi: 10.2166/wst.1999.0036
[20] Jordan M A, Welsh D T, Teasdale P R, Catterall K, John R. A ferricyanide-mediated activated sludge bioassay for fast determination of the biochemical oxygen demand of wastewaters. Water Research, 2010, 44(20): 5981−5988 doi: 10.1016/j.watres.2010.07.042
[21] Gustaf O, Bob N. Wastewater wreatment systems: Modelling diagnosis and control. Magazine of the International Water Association, 1999, 2: 43−44
[22] Lin C K, Katayama Y, Hosomi M, Murakami A, Okada M. The characteristics of the bacterial community structure and population dynamics for phosphorus removal in SBR activated sludge processes. Water Research, 2003, 37(12): 2944−2952 doi: 10.1016/S0043-1354(02)00568-7
[23] Ni B J, Yuan Z G. Recent advances in mathematical modeling of nitrous oxides emissions from wastewater treatment processes. Water Research, 2015, 87: 336−346 doi: 10.1016/j.watres.2015.09.049
[24] 乔俊飞, 卢超, 王磊, 韩红桂. 城市污水处理过程模型研究综述. 信息与控制, 2018, 47(2): 129−139

Qiao Jun-Fei, Lu Chao, Wang Lei, Han Hong-Gui. Models of urban wastewater treatment process: An overview. Information and Control, 2018, 47(2): 129−139
[25] 于广平, 苑明哲, 王宏. 活性污泥法污水处理数学模型的发展和应用. 信息与控制, 2006, 35(5): 614−618, 623 doi: 10.3969/j.issn.1002-0411.2006.05.014

Yu Guang-Ping, Fan Ming-Zhe, Wang Hong. Development and application of activated sludge mathematical models for wastewater treatment process. Information and Control, 2006, 35(5): 614−618, 623 doi: 10.3969/j.issn.1002-0411.2006.05.014
[26] Gernaey K V, Rosen C, Jeppsson U. WWTP dynamic disturbance modelling -an essential module for long-term benchmarking development. Water Science and Technology, 2006, 53(4−5): 225−234 doi: 10.2166/wst.2006.127
[27] Phuc B D H, You S S, Hung B M, Kim H S. Robust control synthesis for the activated sludge process. Environmental Science: Water Research & Technology, 2018, 4(7): 992−1001
[28] Henze M, Grady C P L, Gujer W, Marais G V R, Matsuo T. Activated Sludge Model No. 1, Technical Report No. 1, IAWPRC, 1987. 195−214
[29] Henze M, Gujer W, Mino T. Activated Sludge Model No. 2. International Association on Water Pollution Research and Control Scientific and Technical Reports, 1995, 31(2): 1−11 doi: 10.2166/wst.1995.0061
[30] Gujer W, Henze M, Mino T, van Loosdrecht M. Activated sludge model no. 3. Water Science & Technology, 1999, 39(1): 183−193
[31] Pallavhee T, Sundaramoorthy S, Sivasankaran M A. Optimal control of small size single tank activated sludge process with regulated aeration and external carbon addition. Industrial & Engineering Chemistry Research, 2018, 57(46): 15811−15823
[32] Mannina G, Cosenza A, Viviani G. Uncertainty assessment of a model for biological nitrogen and phosphorus removal: Application to a large wastewater treatment plant. Physics and Chemistry of the Earth, Parts A/B/C, 2012, 42−44: 61−69 doi: 10.1016/j.pce.2011.04.008
[33] 彭永臻, 王宝贞, 王淑莹. 活性污泥法的多变量最优控制I. 基础理论与DO浓度对运行费用的影响. 环境科学学报, 1998, 18(1): 11−19 doi: 10.3321/j.issn:0253-2468.1998.01.002

Peng Yong-Zhen, Wang Bao-Zhen, Wang Shu-Ying. Multivariable optimal control of activated sludge process: I. Basic theory and effect of DO on operational cost. Acta Scientiae Circumstantiae, 1998, 18(1): 11−19 doi: 10.3321/j.issn:0253-2468.1998.01.002
[34] El Shorbagy W E, Radif N N, Droste R L. Optimization of A.2O BNR processes using ASM and EAWAG Bio-P models: Model performance. Water Environment Research, 2013, 85(12): 2271−2284 doi: 10.2175/106143013X13596524517102
[35] Sun J Y, Liang P, Yan X X, Zuo K C, Xiao K, Xia J L, et al. Reducing aeration energy consumption in a large-scale membrane bioreactor: Process simulation and engineering application. Water Research, 2016, 93: 205−213 doi: 10.1016/j.watres.2016.02.026
[36] Chen W L, Lu X W, Yao C H. Optimal strategies evaluated by multi-objective optimization method for improving the performance of a novel cycle operating activated sludge process. Chemical Engineering Journal, 2015, 260: 492−502 doi: 10.1016/j.cej.2014.08.087
[37] 杜树新. 污水生化处理过程建模与控制. 控制理论与应用, 2002, 19(5): 660−666 doi: 10.3969/j.issn.1000-8152.2002.05.002

Du Shu-Xin. Modeling and control of biological wastewater treatment processes. Control Theory & Applications, 2002, 19(5): 660−666 doi: 10.3969/j.issn.1000-8152.2002.05.002
[38] 董姗燕, 李咏梅, 池春榕, 刘祖文. 基于活性污泥数学模型(ASMs)的污水处理系统不确定性分析研究进展. 化工进展, 2017, 36(12): 4651−4657

Dong Shan-Yan, Li Yong-Mei, Chi Chun-Rong, Liu Zu-Wen. Research and development on uncertainty analysis in wastewater treatment system based on activated sludge model(ASMs). Chemical Industry and Engineering Progress, 2017, 36(12): 4651−4657
[39] Spinelli M, Eusebi A L, Vasilaki V, Katsou E, Frison N, Cingolani D, et al. Critical analyses of nitrous oxide emissions in a full scale activated sludge system treating low carbon-to-nitrogen ratio wastewater. Journal of Cleaner Production, 2018, 190: 517−524 doi: 10.1016/j.jclepro.2018.04.178
[40] Iacopozzi I, Innocenti V, Marsili-Libelli S, Giusti E. A modified activated sludge model no. 3 (ASM3) with two-step nitrification- denitrification. Environmental Modelling & Software, 2007, 22(6): 847−861
[41] Rieger L, Koch G, Kühni M, Gujer W, Siegrist H. The eawag Bio-P module for activated sludge model no. 3. Water Research, 2001, 35(16): 3887−3903 doi: 10.1016/S0043-1354(01)00110-5
[42] Zhao H, Hao O J, McAvoy T J. Approaches to modeling nutrient dynamics: ASM2, simplified model and neural nets. Water Science and Technology, 1999, 39(1): 227−234 doi: 10.2166/wst.1999.0048
[43] Jeppsson U, Alex J, Batstone D J, Benedetti L, Comas J, Copp J B, et al. Benchmark simulation models, quo vadis. Water Science & Technology, 2013, 68(1): 1−15
[44] Jeppsson U, Pons M N, Nopens I, Alex J, Copp J B, Gernaey K V, et al. Benchmark simulation model no 2: General protocol and exploratory case studies. Water Science & Technology, 2007, 56(8): 67−78
[45] Jeppsson U, Rosen C, Alex J, Copp J, Gernaey K V, Pons M N, et al. Towards a benchmark simulation model for plant-wide control strategy performance evaluation of WWTPs. Water Science & Technology, 2006, 53(1): 287−295
[46] Saagi R, Flores-Alsina X, Kroll S, Gernaey K V, Jeppsson U. A model library for simulation and benchmarking of integrated urban wastewater systems. Environmental Modelling & Software, 2017, 93: 282−295
[47] Rosen C, Jeppsson U, Vanrolleghem P A. Towards a common benchmark for long-term process control and monitoring performance evaluation. Water Science Technology, 2004, 50(11): 41−49 doi: 10.2166/wst.2004.0669
[48] Gernaey K V, Jørgensen S B. Benchmarking combined biological phosphorus and nitrogen removal wastewater treatment processes. Control Engineering Practice, 2004, 12(3): 357−373 doi: 10.1016/S0967-0661(03)00080-7
[49] Shen W H, Chen X Q, Corriou J P. Application of model predictive control to the BSM1 benchmark of wastewater treatment process. Computers & Chemical Engineering, 2008, 32(12): 2849−2856
[50] Nopens I, Benedetti L, Jeppsson U, Pons M N, Alex J, Copp J B, et al. Benchmark simulation model no 2: Finalisation of plant layout and default control strategy. Water Science & Technology, 2010, 62(9): 1967−1974
[51] Maere T, Verrecht B, Moerenhout S, Judd S, Nopens I. BSM-MBR: A benchmark simulation model to compare control and operational strategies for membrane bioreactors. Water Research, 2011, 45(6): 2181−2190 doi: 10.1016/j.watres.2011.01.006
[52] 王藩, 王小艺, 魏伟, 许继平, 蒋耘伟, 王灵宇. 基于BSM1的城市污水处理优化控制方案研究. 控制工程, 2015, 22(6): 1224−1229

Wang Pan, Wang Xiao-Yi, Wei Wei, Xu Ji-Ping, Jiang Yun-Wei, Wang Ling-Yu. Optimal solution to wastewater treatment process control strategy based on benchmark model no. 1. Control Engineering of China, 2015, 22(6): 1224−1229
[53] Sweetapple C, Fu G T, Butler D. Multi-objective optimisation of wastewater treatment plant control to reduce greenhouse gas emissions. Water Research, 2014, 55: 52−62 doi: 10.1016/j.watres.2014.02.018
[54] Plósz B G. Optimization of the activated sludge anoxic reactor configuration as a means to control nutrient removal kinetically. Water Research, 2007, 41(8): 1763−1773 doi: 10.1016/j.watres.2007.01.007
[55] De Gussem K, Fenu A, Wambecq T, Weemaes M. Energy saving on wastewater treatment plants through improved online control: Case study wastewater treatment plant antwerp-south. Water Science & Technology, 2014, 69(5): 1074−1079
[56] Yang Y, Yang J K, Zuo J L, Li Y, He S, Yang X, et al. Study on two operating conditions of a full-scale oxidation ditch for optimization of energy consumption and effluent quality by using CFD model. Water Research, 2011, 45(11): 3439−3452 doi: 10.1016/j.watres.2011.04.007
[57] Andraka D. Reliability analysis of activated sludge process by means of biokinetic modelling and simulation results. Water, 2020, 12(1): Article No. 291 doi: 10.3390/w12010291
[58] Yang C L, Qiao J F, Wang L, Zhu X X. Dynamical regularized echo state network for time series prediction. Neural Computing and Applications, 2019, 31(10): 6781−6794 doi: 10.1007/s00521-018-3488-z
[59] Corominas L, Garrido-Baserba M, Villez K, Olsson G, Cortés U, Poch M. Transforming data into knowledge for improved wastewater treatment operation: A critical review of techniques. Environmental Modelling & Software, 2018, 106: 89−103
[60] Lou I, Zhao Y C. Sludge bulking prediction using principle component regression and artificial neural network. Mathematical Problems in Engineering, 2012, 2012: Article No. 237693
[61] Newhart K B, Holloway R W, Hering A S, Cath T Y. Data-driven performance analyses of wastewater treatment plants: A review. Water Research, 2019, 157: 498−513 doi: 10.1016/j.watres.2019.03.030
[62] Han H G, Zhu S G, Qiao J F, Guo M. Data-driven intelligent monitoring system for key variables in wastewater treatment process. Chinese Journal of Chemical Engineering, 2018, 26(10): 2093−2101 doi: 10.1016/j.cjche.2018.03.027
[63] Yoo C K, Vanrolleghem P A, Lee I B. Nonlinear modeling and adaptive monitoring with fuzzy and multivariate statistical methods in biological wastewater treatment plants. Journal of Biotechnology, 2003, 105(1−2): 135−163 doi: 10.1016/S0168-1656(03)00168-8
[64] Dürrenmatt D J, Gujer W. Data-driven modeling approaches to support wastewater treatment plant operation. Environmental Modelling & Software, 2012, 30: 47−56
[65] Zeng Y H, Zhang Z J, Kusiak A, Tang F, Wei X P. Optimizing wastewater pumping system with data-driven models and a greedy electromagnetism-like algorithm. Stochastic Environmental Research and Risk Assessment, 2016, 30(4): 1263−1275 doi: 10.1007/s00477-015-1115-4
[66] Filipe J, Bessa R J, Reis M, Alves R, Póvoa P. Data-driven predictive energy optimization in a wastewater pumping station. Applied Energy, 2019, 252: Article No. 113423 doi: 10.1016/j.apenergy.2019.113423
[67] Asadi A, Verma A, Yang K, Mejabi B. Wastewater treatment aeration process optimization: A data mining approach. Journal of Environmental Management, 2017, 203: 630−639 doi: 10.1016/j.jenvman.2016.07.047
[68] Huang W, Oh S K, Pedrycz W. Hybrid fuzzy wavelet neural networks architecture based on polynomial neural networks and fuzzy set/relation inference-based wavelet neurons. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(8): 3452−3462 doi: 10.1109/TNNLS.2017.2729589
[69] Kobayashi M. Decomposition of rotor hopfield neural networks using complex numbers. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(4): 1366−1370 doi: 10.1109/TNNLS.2017.2657781
[70] Kang Q, Shi L, Zhou M C, Wang X S, Wu Q D, Wei Z. A distance-based weighted undersampling scheme for support vector machines and its application to imbalanced classification. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(9): 4152−4165 doi: 10.1109/TNNLS.2017.2755595
[71] Gangopadhyay A, Chatterjee O, Chakrabartty S. Extended polynomial growth transforms for design and training of generalized support vector machines. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(5): 1961−1974 doi: 10.1109/TNNLS.2017.2690434
[72] Tanaka G, Nakane R, Takeuchi T, Yamane T, Nakano D, Katayama Y, et al. Spatially arranged sparse recurrent neural networks for energy efficient associative memory. IEEE Transactions on Neural Networks and Learning Systems, 2020, 31(1): 24−38 doi: 10.1109/TNNLS.2019.2899344
[73] Miller M W, Elliott M, DeArmond J, Kinyua M, Wett B, Murthy S, et al. Controlling the COD removal of an a-stage pilot study with instrumentation and automatic process control. Water Science & Technology, 2017, 75(11): 2669−2679
[74] Chen W C, Chang N B, Shieh W K. Advanced hybrid fuzzy-neural controller for industrial wastewater treatment. Journal of Environmental Engineering, 2001, 127(11): 1048−1059 doi: 10.1061/(ASCE)0733-9372(2001)127:11(1048)
[75] Manu D S, Thalla A K. Artificial intelligence models for predicting the performance of biological wastewater treatment plant in the removal of kjeldahl nitrogen from wastewater. Applied Water Science, 2017, 7(7): 3783−3791 doi: 10.1007/s13201-017-0526-4
[76] Qiao J F, Wang G M, Li W J, Li X L. A deep belief network with PLSR for nonlinear system modeling. Neural Networks, 2018, 104: 68−79 doi: 10.1016/j.neunet.2017.10.006
[77] Chen Z B, Nie S K, Ren N Q, Chen Z Q, Wang H C, Cui M H. Improving the efficiencies of simultaneous organic substance and nitrogen removal in a multi-stage loop membrane bioreactor-based PWWTP using an on-line knowledge-based expert system. Water Research, 2011, 45(16): 5266−5278 doi: 10.1016/j.watres.2011.07.032
[78] 蒙西, 乔俊飞, 韩红桂. 基于类脑模块化神经网络的污水处理过程关键出水参数软测量. 自动化学报, 2019, 45(5): 906−919

Meng Xi, Qiao Jun-Fei, Han Hong-Gui. Soft measurement of key effluent parameters in wastewater treatment process usings brain-like modular neural networks. Acta Automatica Sinica, 2019, 45(5): 906−919
[79] 王丽娟. 基于主元分析和神经网络的污水处理能耗分析. 计算机技术与发展, 2011, 21(3): 243−245 doi: 10.3969/j.issn.1673-629X.2011.03.062

Wang Li-Juan. Analysis of sewage treatment aeration energy consumption based on PCA and BP networks. Computer Technology and Development, 2011, 21(3): 243−245 doi: 10.3969/j.issn.1673-629X.2011.03.062
[80] Zhang Z J, Kusiak A, Zeng Y H, Wei X P. Modeling and optimization of a wastewater pumping system with data-mining methods. Applied Energy, 2016, 164: 303−311 doi: 10.1016/j.apenergy.2015.11.061
[81] Torregrossa D, Hansen J, Hernández-Sancho F, Cornelissen A, Schutz G, Leopold U. A data-driven methodology to support pump performance analysis and energy efficiency optimization in waste water treatment plants. Applied Energy, 2017, 208: 1430−1440 doi: 10.1016/j.apenergy.2017.09.012
[82] Qiao J F, Zhang W. Dynamic multi-objective optimization control for wastewater treatment process. Neural Computing and Applications, 2018, 29(11): 1261−1271 doi: 10.1007/s00521-016-2642-8
[83] Zhang R, Xie W M, Yu H Q, Li W W. Optimizing municipal wastewater treatment plants using an improved multi-objective optimization method. Bioresource Technology, 2014, 157: 161−165 doi: 10.1016/j.biortech.2014.01.103
[84] 韩红桂, 张璐, 乔俊飞. 基于多目标粒子群算法的污水处理智能优化控制. 化工学报, 2017, 68(4): 1474−1481

Han Hong-Gui, Zhang Lu, Qiao Jun-Fei. Intelligent optimal control for wastewater treatment based on multi-objective particle swarm algorithm. CIESC Journal, 2017, 68(4): 1474−1481
[85] Santín I, Pedret C, Vilanova R. Applying variable dissolved oxygen set point in a two level hierarchical control structure to a wastewater treatment process. Journal of Process Control, 2015, 28: 40−55 doi: 10.1016/j.jprocont.2015.02.005
[86] Amand L, Carlsson B. Optimal aeration control in a nitrifying activated sludge process. Water Research, 2012, 46(7): 2101−2110 doi: 10.1016/j.watres.2012.01.023
[87] Martín J M, Vega P, Revollar S. Set-point optimization for enhancing the MPC control of the n-removal process in WWTP’s. In: Proceedings of the 2012 World Automation Congress 2012. Puerto Vallarta, Mexico: IEEE, 2012. 1−6
[88] Duzinkiewicz K, Brdys M A, Kurek W, Piotrowski R. Genetic hybrid predictive controller for optimized dissolved-oxygen tracking at lower control level. IEEE Transactions on Control Systems Technology, 2009, 17(5): 1183−1192 doi: 10.1109/TCST.2008.2004499
[89] Sharma A K, Guildal T, Thomsen H R, Jacobsen B N. Energy savings by reduced mixing in aeration tanks: Results from a full scale investigation and long term implementation at avedoere wastewater treatment plant. Water Science & Technology, 2011, 64(5): 1089−1095
[90] Chachuat B, Roche N, Latifi M A. Optimal aeration control of industrial alternating activated sludge plants. Biochemical Engineering Journal, 2005, 23(3): 277−289 doi: 10.1016/j.bej.2005.01.012
[91] 张平, 苑明哲, 王宏. 前置反硝化污水生化处理过程优化控制. 信息与控制, 2008, 37(1): 113−118 doi: 10.3969/j.issn.1002-0411.2008.01.019

Zhang Ping, Yuan Ming-Zhe, Wang Hong. Optimization control for pre-denitrification type of biological treatment process for wastewater. Information and control, 2008, 37(1): 113−118 doi: 10.3969/j.issn.1002-0411.2008.01.019
[92] 韩广, 乔俊飞, 韩红桂, 柴伟. 基于Hopfield神经网络的污水处理过程优化控制. 控制与决策, 2014, 29(11): 2085−2088

Han Guang, Qiao Jun-Fei, Han Hong-Gui, Chai Wei. Optimal control for wastewater treatment process based on Hopfield neural network. Control and Decision, 2014, 29(11): 2085−2088
[93] 许玉格, 宋亚龄, 罗飞, 张雍涛, 曹涛. 基于人工免疫算法的污水处理系统节能优化. 华南理工大学学报(自然科学版), 2013, 41(8): 34−40

Xu Yu-Ge, Song Ya-Ling, Luo Fei, Zhang Yong-Tao, Cao Tao. Energy-saving optimization of wastewater treatment system based on artificial immune algorithm. Journal of South China University of Technology (Natural Science Edition), 2013, 41(8): 34−40
[94] 刘载文, 张春芝, 王小艺, 薛福霞, 程志强. 基于遗传算法的污水处理过程优化控制方法. 计算机与应用化学, 2007, 24(7): 959−962 doi: 10.3969/j.issn.1001-4160.2007.07.026

Liu Zai-Wen, Zhang Chun-Zhi, Wang Xiao-Yi, Xue Fu-Xia, Cheng Zhi-Qiang. Method of optimal control for wastewater treatment process based on genetic algorithms. Computers and Applied Chemistry, 2007, 24(7): 959−962 doi: 10.3969/j.issn.1001-4160.2007.07.026
[95] Ruano M V, Ribes J, Seco A, Ferrer J. An advanced control strategy for biological nutrient removal in continuous systems based on pH and ORP sensors. Chemical Engineering Journal, 2012, 183: 212−221 doi: 10.1016/j.cej.2011.12.064
[96] Sadeghassadi M, Macnab C J B, Gopaluni B, Westwick D. Application of neural networks for optimal-setpoint design and MPC control in biological wastewater treatment. Computers & Chemical Engineering, 2018, 115: 150−160
[97] Qiao J F, Bo Y C, Chai W, Han H G. Adaptive optimal control for a wastewater treatment plant based on a data-driven method. Water Science & Technology, 2013, 67(10): 2314−2320
[98] Vega P, Revollar S, Francisco M, Martín J M. Integration of set point optimization techniques into nonlinear MPC for improving the operation of WWTPs. Computers & Chemical Engineering, 2014, 68: 78−95
[99] 史雄伟, 乔俊飞, 苑明哲. 基于改进粒子群优化算法的污水处理过程优化控制. 信息与控制, 2011, 40(5): 698−703

Shi Xiong-Wei, Qiao Jun-Fei, Yuan Ming-Zhe. Optimal control for wastewater treatment process based on improved particle swarm optimization algorithm. Information and Control, 2011, 40(5): 698−703
[100] Schlüter M, Egea J A, Antelo L T, Alonso A A, Banga J R. An extended ant colony optimization algorithm for integrated process and control system design. Industrial & Engineering Chemistry Research, 2009, 48(14): 6723−6738
[101] Yetilmezsoy K. Integration of kinetic modeling and desirability function approach for multi-objective optimization of UASB reactor treating poultry manure wastewater. Bioresource Technology, 2012, 118: 89−101 doi: 10.1016/j.biortech.2012.05.088
[102] Wu M Y, Li K, Kwong S, Zhang Q F, Zhang J. Learning to decompose: A paradigm for decomposition-based multiobjective optimization. IEEE Transactions on Evolutionary Computation, 2019, 23(3): 376−390 doi: 10.1109/TEVC.2018.2865931
[103] Flores-Alsina X, Arnell M, Amerlinck Y, Corominas L, Gernaey K V, Guo L S, et al. Balancing effluent quality, economic cost and greenhouse gas emissions during the evaluation of (plant-wide) control/operational strategies in WWTPs. Science of the Total Environment, 2014, 466−467: 616−624 doi: 10.1016/j.scitotenv.2013.07.046
[104] Cadet C, Béteau J F, Hernandez S C. Multicriteria control strategy for cost/quality compromise in wastewater treatment plants. Control Engineering Practice, 2004, 12(3): 335−347 doi: 10.1016/S0967-0661(03)00134-5
[105] Hakanen J, Sahlstedt K, Miettinen K. Wastewater treatment plant design and operation under multiple conflicting objective functions. Environmental Modelling & Software, 2013, 46: 240−249
[106] Reifsnyder S, Garrido-Baserba M, Cecconi F, Wong L, Ackman P, Melitas N, et al. Relationship between manual air valve positioning, water quality and energy usage in activated sludge processes. Water Research, 2020, 173: Article No. 115537 doi: 10.1016/j.watres.2020.115537
[107] 杨壮, 杨翠丽, 顾锞, 乔俊飞. 多目标进化算法的污水处理过程优化控制. 控制理论与应用, 2020, 37(1): 169−175 doi: 10.7641/CTA.2019.80408

Yang Zhuang, Yang Cui-Li, Gu Ke, Qiao Jun-Fei. Multi-objective evolutionary algorithm for wastewater treatment process optimization control. Control Theory & Applications, 2020, 37(1): 169−175 doi: 10.7641/CTA.2019.80408
[108] Hreiz R, Roche N, Benyahia B, Latifi M A. Multi-objective optimal control of small-size wastewater treatment plants. Chemical Engineering Research and Design, 2015, 102: 345−353 doi: 10.1016/j.cherd.2015.06.039
[109] Egea J A, Gracia I. Dynamic multiobjective global optimization of a waste water treatment plant for nitrogen removal. IFAC Proceedings Volumes, 2012, 45(2): 374−379 doi: 10.3182/20120215-3-AT-3016.00066
[110] Qiao J F, Hou Y, Han H G. Optimal control for wastewater treatment process based on an adaptive multi-objective differential evolution algorithm. Neural Computing and Applications, 2017, 31(7): 2537−2550
[111] De Faria A B B, Ahmadi A, Tiruta-Barna L, Sperandio M. Feasibility of rigorous multi-objective optimization of wastewater management and treatment plants. Chemical Engineering Research and Design, 2016, 115: 394−406 doi: 10.1016/j.cherd.2016.09.005
[112] Han H G, Wu X L, Zhang L, Tian Y, Qiao J F. Self-organizing RBF neural network using an adaptive gradient multiobjective particle swarm optimization. IEEE Transactions on Cybernetics, 2019, 49(1): 69−82 doi: 10.1109/TCYB.2017.2764744
[113] Li F J, Qiao J F, Han H G, Yang C L. A self-organizing cascade neural network with random weights for nonlinear system modeling. Applied Soft Computing, 2016, 42: 184−193 doi: 10.1016/j.asoc.2016.01.028
[114] Han H G, Qiao J F. A structure optimisation algorithm for feedforward neural network construction. Neurocomputing, 2013, 99: 347−357 doi: 10.1016/j.neucom.2012.07.023
[115] Poch M, Comas J, Porro J, Garrido-Baserba M, Corominas L, Pijuan M. Where are we in wastewater treatment plants data management? A review and a proposal. In: Proceedings of the 7th International Congress on Environmental Modelling and Software: Bold Visions for Environmental Modeling. San Diego, USA: iEMSs, 2014. 221−234
[116] 李文静, 李萌, 乔俊飞. 基于互信息和自组织RBF神经网络的出水BOD软测量方法. 化工学报, 2019, 70(2): 687−695

Li Wen-Jing, Li Meng, Qiao Jun-Fei. Effluent BOD soft measurement based on mutual information and self-organizing RBF neural network. CIESC Journal, 2019, 70(2): 687−695
[117] Wilén B M, Liebana R, Persson F, Modin O, Hermansson M. The mechanisms of granulation of activated sludge in wastewater treatment, its optimization, and impact on effluent quality. Applied Microbiology and Biotechnology, 2018, 102(12): 5005−5020 doi: 10.1007/s00253-018-8990-9
[118] 李永明, 史旭东, 熊伟丽. 基于工况识别的污水处理过程多目标优化控制. 化工学报, 2019, 70(11): 4325−4336

Li Yong-Ming, Shi Xu-Dong, Xiong Wei-Li. Condition recognition based intelligent multi-objective optimal control for wastewater treatment. CIESC Journal, 2019, 70(11): 4325−4336
[119] Aulinas M, Nieves J C, Cortés U, Poch M. Supporting decision making in urban wastewater systems using a knowledge-based approach. Environmental Modelling & Software, 2011, 26(5): 562−572
[120] Castillo A, Cheali P, Gómez V, Comas J, Poch M, S in, G. An integrated knowledge-based and optimization tool for the sustainable selection of wastewater treatment process concepts. Environmental Modelling & Software, 2016, 84: 177−192
[121] Rizzo D B, Blackburn M R. Harnessing expert knowledge: Defining Bayesian network model priors from expert knowledge only—prior elicitation for the vibration qualification problem. IEEE Systems Journal, 2019, 13(2): 1895−1905 doi: 10.1109/JSYST.2019.2892942
[122] Garrido-Baserba M, Reif R, Hernández F, Poch M. Implementation of a knowledge-based methodology in a decision support system for the design of suitable wastewater treatment process flow diagrams. Journal of Environmental Management, 2012, 112: 384−391 doi: 10.1016/j.jenvman.2012.08.013
[123] Han H G, Zhang L, Liu H X, Yang C L, Qiao J F. Intelligent optimal control system with flexible objective functions and its applications in wastewater treatment process. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019. DOI: 10.1109/TSMC.2019.2927631
[124] Azzouz R, Bechikh S, Said L B. A dynamic multi-objective evolutionary algorithm using a change severity-based adaptive population management strategy. Soft Computing, 2017, 21(4): 885−906 doi: 10.1007/s00500-015-1820-4
[125] Chen G Y, Li J H. A diversity ranking based evolutionary algorithm for multi-objective and many-objective optimization. Swarm and Evolutionary Computation, 2019, 48: 274−287 doi: 10.1016/j.swevo.2019.03.009
[126] Zhang S H, Wang J Y, Guo Z H. Research on combined model based on multi-objective optimization and application in time series forecast. Soft Computing, 2019, 23(22): 11493−11521 doi: 10.1007/s00500-018-03690-w
[127] Barabasz B, Barrett S, Siwik L, Łoś M, Podsiadło K, Woźniak M. Speeding up multi-objective optimization of liquid fossil fuel reserve exploitation with parallel hybrid memory integration. Journal of Computational Science, 2019, 31: 126−136 doi: 10.1016/j.jocs.2019.01.001
[128] Singh H K, Isaacs A, Ray T. A Pareto corner search evolutionary algorithm and dimensionality reduction in many-objective optimization problems. IEEE Transactions on Evolutionary Computation, 2011, 15(4): 539−556 doi: 10.1109/TEVC.2010.2093579
[129] Yang C L, Qiao J F, Ahmad Z, Nie K Z, Wang L. Online sequential echo state network with sparse RLS algorithm for time series prediction. Neural Networks, 2019, 118: 32−42 doi: 10.1016/j.neunet.2019.05.006
[130] 乔俊飞, 韩改堂, 周红标. 基于知识的污水生化处理过程智能优化方法. 自动化学报, 2017, 43(6): 1038−1046

Qiao Jun-Fei, Han Gai-Tang, Zhou Hong-Biao. Knowledge-based intelligent optimal control for wastewater biochemical treatment process. Acta Automatica Sinica, 2017, 43(6): 1038−1046
[131] 钱锋, 杜文莉, 钟伟民, 唐漾. 石油和化工行业智能优化制造若干问题及挑战. 自动化学报, 2017, 43(6): 893−901

Qian Feng, Du Wen-Li, Zhong Wei-Min, Tang Yang. Problems and challenges of smart optimization manufacturing in petrochemical industries. Acta Automatica Sinica, 2017, 43(6): 893−901
[132] 桂卫华, 阳春华, 陈晓方, 王雅琳. 有色冶金过程建模与优化的若干问题及挑战. 自动化学报, 2013, 39(3): 197−207 doi: 10.1016/S1874-1029(13)60022-1

Gui Wei-Hua, Yang Chun-Hua, Chen Xiao-Fang, Wang Ya-Lin. Modeling and optimization problems and challenges arising in nonferrous metallurgical processes. Acta Automatica Sinica, 2013, 39(3): 197−207 doi: 10.1016/S1874-1029(13)60022-1
[133] 柴天佑. 复杂工业过程运行优化与反馈控制. 自动化学报, 2013, 39(11): 1744−1757 doi: 10.3724/SP.J.1004.2013.01744

Chai Tian-You. Operational optimization and feedback control for complex industrial processes. Acta Automatica Sinica, 2013, 39(11): 1744−1757 doi: 10.3724/SP.J.1004.2013.01744